A Data-Driven Approach to Marketing Analytics

This article will closely examine three significant categories of data-driven marketing analytics: descriptive, predictive, and prescriptive.

What is data-driven analytics?

In the broadest sense, data-driven marketing means deploying quantitative analytic methods to analyze and extract actionable meaning from data effectively. This allows marketing analysts and stakeholders to develop informed marketing choices and deliverables. We live in a renaissance of data, where enormous amounts previously unavailable to analysts are now at our fingertips. This phenomenon shows no signs of stopping in the future, so data-driven marketing analytics is more crucial than ever. According to a Fortune Business Insights report published in July 2022, the global big data analytics market in 2021 reached $240.56 billion in 2021 and is expected to rise to $655.53 billion by 2029. Depending on the industry, a 2015 McKinsey brief found that big data and advanced analytics yielded anywhere from a one percent to 60 percent increase in revenue and cost improvement.

In numerous cases over the past decade, companies have centered their work on more broadly-targeted methods to derive novel information from big data and advanced analytics. Enterprising data scientists have helmed this work, and analytic vendors, are keen on diving into the data and creatively running analyses to unearth nuggets of knowledge and insights. Depending on the workflow stage(s) and data analytics requirements, there are three major categories of data-driven marketing analytics: descriptive analytics, predictive analytics, and prescriptive analytics. The categories encapsulate an array of analytic techniques, which encapsulate but are not limited to statistical modeling, machine learning, data mining, and AI.

What is descriptive analytics?

Descriptive analytics is a category of data-driven analytics that engages in interpretative and analytic work on historical data. Descriptive analytics’ primary purpose is to locate past patterns and trends retroactively. This is extremely useful for businesses because this analysis helps uncover future opportunities or potential issues. A 2020 research article in Nature Machine Intelligence analyzed combining procedural content generation (PCG) from the gaming industry and machine learning (ML) methods. Using PCG, an older technology that algorithmically generates a game’s content, along with ML, allows researchers and analysts to randomize problem parameters during analysis. This version of descriptive analytics analyzes past issues with machine learning models being too specific and will enable PCG to be married to ML to open up new opportunities.

Another paper published in 2019 by the Journal of Marketing Research proposed an analytic methodology to examine co-occurrences of products in consumer carts. They focused on contemporary advances in ML and natural language processing (NLP). Their process is already highly adaptable and scalable for the retail industry because data from online checkout and cart systems are readily available. When paired with an analysis of cross-product co-occurrence, complementarity, and intra-category substitution, this methodology is a promising approach to descriptive analytics by analyzing past trends of co-occurrent products. 

What is predictive analytics?

Predictive analytics is a category of data-driven analytics that analyzes current and/or historical data using statistical methods. Predictive analytics’ primary purpose is, as the name suggests, to analyze the likelihood of an event(s) happening in the future. By focusing on the nature of the event and the potentially time-sensitive occurrence of the event, predictive analytics is helpful for several prediction questions in marketing analytics. These problems include credit scoring, customer churn and turnover, customer acquisition, lifetime value, advertising click rates, purchase frequency, and product recommendations. Predictive analytics can assist in optimizing and improving many analytic areas, including efficiency, risk management and reduction, and customer service. 

For example, a 2021 article published in ACM Computing Surveys developed a meta-analysis of current methodologies for predicting user responses in the online advertising industry. By creating a taxonomy of user-response prediction methods, focusing on ML methods, their research can be used to grapple with predictive analytics in other contexts. 

One of the most promising methods for predictive analytics in marketing is deep learning (DL). DL is a subfield of machine learning that focuses on algorithms that take their structure and operations from neural networks in the brain. These artificial neural networks deploy multiple processing layers to derive increasingly more complex features from large amounts of data. This gives DL a reasonably strong prediction accuracy compared to other analytic methods, as it is algorithmically designed to model how the human brain processes certain types of knowledge. Like human neural networks, DL can concurrently analyze multiple data types, such as images, audio, location, clickstream data, app data, and social network data. This ability to process disparate data separates it from other analytic methods. Still, DL is also distinct from the human neural networks based on the sheer scale of data it can process, which is well beyond what the human brain can handle.

However, one of the major complaints about current predictive analytics methods is that they are too opaque, with little possibility of discerning what happens during processing after the initial input. Therefore, it’s essential to always keep in mind the potential limitations of predictive analytics, mainly when relying on algorithms to streamline predictions. For example, a 2022 article in the Journal of Business Research combined a systematic literature review and interviews with ML specialists to identify algorithmic bias in ML-based marketing analytics. The article highlights an array of algorithmic biases that can pervade ML-based marketing analytics, including weak algorithm designs, unrepresentative datasets, ineffective models, or historical human tendencies. The latter problem is especially problematic, as it can lead predictive analytics to replicate stereotypes and biases against marginalized consumers. For example, a 2021 article found that algorithmic models designed to guide clinical decision-making during the COVID-19 pandemic were rushed and biased against minority populations. A 2020 article found that Uber and Lyft’s algorithms set higher prices for destinations in predominantly African-American people. These algorithmic biases weren’t limited to race- a 2019 article published in Management Science found that Facebook’s targeted ad algorithms showed fewer ads advertising STEM jobs to women than men, despite initially intending to be gender-neutral in its delivery. The examples above underscore the importance of balancing predictive accuracy with transparency when working with predictive analytic algorithms for marketing.  

What is prescriptive analytics?

Prescriptive analytics is a category of data-driven analytics that applies testing to derive which outcome will yield the most promising marketing results in a given scenario. Prescriptive analytics’ primary purpose is determining why a potential product will occur or making the best decisions in a particular method. Unlike predictive analytics, prescriptive analytics focuses on the actions needed to attain predicted outcomes and the relational impacts of each step. In that sense, prescriptive analytics encapsulates predictive analytics.

  

Predictive analytics commonly utilizes randomized experimental techniques, such as A/B testing, to determine the best marketing outcomes, such as price point, ad copy, strategy, demographics, etc. Randomized experiments work by randomly distributing the experimental variables across different treatments and identifying the treatment that produces the best outcome. For example, a randomized experiment on ad copy would randomly allocate specific messaging across other conditions, such as consumer age ranges or platforms, and determine which states produced the most effective outcome for the ad copy. Randomization in data-driven analytics allows analysts to reduce bias by equalizing other variables that weren’t directly considered in the experiment’s design. However, randomized experimentation is not always practical for most businesses, as it can be costly and time-consuming. As a result, much of prescriptive analytics is concerned with yielding dynamic choices using outcomes-based predictive analytics and quasi-experimental and observational data.

In a 2021 article published in Marketing Science, researchers focus on “moment marketing,” which allows analysts to synchronize online advertising with offline events in real time. The researchers conducted a causal estimation study by examining significant shifts in (offline) TV advertising costs for a major U.S. fast-food brand over four months in 2017. Using statistical methodologies, the researchers found that advertising focusing on moment-based TV adverts could be used to optimize online sponsored search advertising. This synchronization would produce cross-channel advertising effects. For example, TV advertising can alter the content and platform of online searches, such as how and where consumers search, shortly after a TV ad airs. As a result, the average consumer conducting online investigations reacts differently to search results found “at the moment.” An example of this cross-channel advertising is a Superbowl TV ad spurring viewers to look up a brand or a product in a particular context shortly after the ad airs on television. “Moment marketing” is a generative space for prescriptive analytics, as it allows analysts to understand better how marketing decisions interact and relate to one another.   

Looking Ahead

In the era of big data, companies are increasingly leveraging data-driven analytics to elucidate information that can facilitate business and marketing strategy, deliver better products and services, and target online experiences to consumers. The three major data-driven analytic approaches—descriptive analytics, predictive analytics, and prescriptive analytics— are valuable for businesses working with data. These approaches all complement and ideally occur in tandem with each other.

However, even though analytic models frequently outperform humans, context, reasoning, and final decision-making are ultimately in the hands of analysts. Data-driven analytical approaches are essential tools and methodologies for analysts to rely on. Still, given algorithms’ relative opacity, analysts must continually check their methods for potential biases. 

Finally, recent consumer concerns about privacy in tandem with current laws regulating data privacy have spurred companies to examine how they can make their data more transparent and open to consumers. The future of data-driven analytics in marketing will have to contend with this new expectation of data transparency.

User Segmentation for Product Development

User segmentation is a term that is frequently used by product development teams, but the meaning can be unclear to those unfamiliar with it. In this article, we’ll give a brief primer on user segmentation and its importance to product development, and provide examples of how user segmentation can reveal valuable information about users. 

What is user segmentation?

User segmentation involves analyzing your user base and segmenting it into categories based on user characteristics you can group together, like geographic location, type and duration of product engagement, and demographic information. After segmentation, you can take these groups (or even subgroups) and analyze them further. This will allow you to identify spaces to improve your product or tailor different product experiences to other groups. 

For example, a global subscription streaming company such as Netflix segments their users like:

  • Users in Mexico
  • Users who subscribe to the Premium plan
  • Users who stream on mobile

By analyzing these segments and understanding how they differ and overlap, Netflix can build products and offer services that better meet each segment’s needs.

Why is user segmentation important?

To serve and better target your users, you need to understand them in as much detail as possible. If you don’t conduct user segmentation, you can only understand your users at the individual level or in totality. Gathering information at the individual level is too granular and almost impossible as your user base grows. Trying to figure out information at the global level is too broad, as it forces you to analyze the average of your user base, making it difficult to make any detailed analysis of user behavior and even potentially distorting your data. 

For example, imagine that a company has twenty customers. Fourteen of these customers are aged between 18-24, four are between 30-39, and the remaining two are aged between 70-79. Segmenting users by age makes it apparent that the company is the most successful, with users between 18-24 years of age. However, examining the user base globally would suggest that the average age of a customer is much higher, which would cloak the company’s popularity with younger users.

User segmentation enables you to target smaller groups, which provides you with more accurate information to serve user needs better.

What is user segmentation used for?

User segmentation is used, as mentioned previously, for product development and targeting, by tailoring product experience and services to users. For example, recommending products based on segmented user behavior can lead to higher engagement with the product or service. However, user segmentation can also be used for:

Marketing: Using user segmentation, you can craft messaging and information that is relevant and interesting to your users. Returning to the Netflix example, marketing a premium plan to users using only one screen is unlikely to lead to high conversion rates.

Prioritization: Using user segmentation, you can troubleshoot and solve specific problems for certain user segments that need it rather than attempting to target all users. For example, Netflix offering lower quality aspect ratios is relevant for users with slower Wi-Fi, but it may not apply to everyone. 

Four Types of User Segmentation

There are multiple ways to segment users, but the following four ways are an excellent place to start:

Demographic: Examining identity or characteristic attributes of the user, such as age, income level, or gender
Geographic: Examining where the user is originally from or currently living/engaging with the product
Behavioral: Examining how the user engages with the product, such as frequency of use, duration of use, or average amount of purchase
Technographic: Examining what platform(s) the user uses to engage with the product, such as mobile or desktop

Users can frequently overlap, and it may be necessary to consider these overlaps when analyzing user segments. For example, Netflix may want to examine mobile users in France or premium plan subscribers who are women between the ages of 40-49.

This article is meant to be a basic introduction to the world of user segmentation and give you a foundation to begin your journey into analyzing your user base. User segmentation is a crucial analytic tool for anyone wanting to learn more about their user base and use this data to improve their products and services.

What’s the difference between Google Analytics and product analytics?

More than ever, organizations rise and fall on their ability to gather and leverage data. They need robust interpretation to implement practical changes across teams, departments, and the whole organization. 

But for data to be effectively leveraged, organizations need the right tools to gather the data they need. In product innovation, many organizations fall back on Google Analytics, which is focused on marketing analytics, over a product analytics solution. They use Google Analytics to gather data on user behavior- but the mismatch between what Google Analytics offers and what they need means that companies often fall short of leveraging their data for their directives. 

Google Analytics is a vital tool in its own right, but it’s designed for market analytics rather than product analytics. It’s designed for the early stages of user engagement by collecting and tracking acquisition or where users are coming from. It’s not built to track how users engage with a product, making the work of a product analytics team much more difficult. Because Google Analytics is not equipped to track user retention and engagement with the product, which is a much later stage of user engagement, product analytics are left without the data they need to produce valuable, actionable insights. 

To get these insights, product analytics teams need tools developed explicitly for product analytics, with features targeted at tracking user engagement and retention, such as cohort analysis, user journey comparisons, and segmentation analytics. 

But beyond this, what are the differences between Google Analytics and a product analytics tool?

Google Analytics

Google Analytics is the gold standard of marketing analytics tools. It’s a workhorse used by marketing analytics teams the world over. These teams use three different Google Analytics products:

Google Analytics 4

Google Analytics is the standard, free tool that is the most widely used by businesses and marketing teams.

Google Analytics 360

Google Analytics 360 is the paid, premium version of Google Analytics, which offers additional features like Display & Video 360, Campaign Manager, and machine learning models.

Firebase

Firebase is Google Analytics’ tool explicitly tailored for tracking apps.

All of Google Analytics tools are designed to provide marketing teams clarity on which marketing directives lead to achieving their goals. The primary purpose of these tools is to assist marketing teams with adapting their marketing budgets and actions, optimizing their work towards what brings about the ideal user journey or attribution. Google Analytics helps marketing teams track key performance indicators (KPIs) like first-touch attribution, bounce and exit rates, and average session duration. While this is very valuable for marketers who want to collect information on marketing KPIs, such as traffic sources, page views, time on site, and completion of user outcomes, Google Analytics has few metrics on product KPIs that focus on user engagement, conversion, and retention. 

In addition to Google Analytics, Google recently launched Google Analytics 4 property (formerly App + Web) that tracks and processes web and app data combined. While this tool is promising for teams that need a combined view across web and app platforms, it still has gaps in meeting product analytics needs. Like Google Analytics, it is inadequate for gathering information on product KPIs. 

Luckily for product analytics teams, there are other solutions. 

Product Analytics

Product analytics tools provide information about the later stages of the user journey, namely how users engage with the websites and applications that product teams develop. They help product analytics teams answer behavioral questions such as:

  • Why do some users convert? Why do other users not convert?
  • How is retention linked to different user cohorts? What are the fluctuations in retention when users engage with other features?
  • What are the most prominent factors leading to user engagement and retention?
  • Who are your power/super users? What makes their behavior different from other users?
  • Is the release of a new feature correlated to or even caused the desired change in user behavior?

If teams use Google Analytics, the questions above are tough to answer. Answering questions about user behavior requires more sensitive and granular measurement, which Google Analytics simply isn’t built for. While Google Analytics relies on general, anonymized traffic data, product analytics tools use a tracking model centering on events. Products analytics tools track, on a much more granular level, the actions users take to engage with a product, like sign-ups, downloads, and uploads. By treating these events like nodes, product analytics tools link actions and behaviors to a single user, thus providing discrete insights into how each user’s behavior develops throughout the user journey. This allows product analytics tools to provide much more in-depth information about user behavior and answer the questions that product analytics must pose. It makes these tools a much better fit to drive product improvement and innovation, as it’s tough to improve products without understanding how users engage with them.

Product analytics tools boast a wide range of features for tracking user behavior, including event tracking, user cohort trends, powerful segmentation capacities, and easy access to in-depth analysis of user behavior. They assist product analytic teams, and product developers innovate their products even further.

But- do you need both?

In short, yes. Google Analytics and product analytics tools are structured for fundamentally different aims and needs. Google Analytics is a robust tool for marketing teams working on analyzing traffic to optimize marketing KPIs. On the other hand, product analytics tools work the best for product teams working on product innovation and need detailed information on user behavior. The two are not interchangeable and, in some sense, are entangled with each other.

When marketing and product teams use the tools that best fit their needs, the organization benefits from optimized innovation and growth. Marketing teams engage in market analysis which brings in new customers and allows product teams to rely on a more extensive user base to learn more about user behavior and improve engagement, retention, and conversion. Organizations can identify power/super users to convert into product advocates, which helps drive marketing. In a parallel sense, organizations can identify users with less engagement to whom they should market differently, to reduce bounce and exit rates. Satisfied users bring in more satisfied users. But organizations need product analytics tools to provide the in-depth information required to understand these users’ behavior.

Top session recording tools

What is Session Recording?

Session recording is a process in which website or mobile App visitors browsing behavior is captured, stored, and viewed back at a later time. You can identify a user’s entire journey on your website or app using session recording tools, like clicks, mouse movements, scrolls, etc. You can remember the user’s entire browsing journey from the recorded session and identify friction areas to optimize the user experience and ultimately increase the conversion rate. It also helps to visually determine where the user drops from your marketing or sales funnel from the session recording. The data collected from the session recordings are used to run Machine Learning models to predict when users click the buy button and abandon the site.

What are the use cases for Session Recording?

Following are some of the use cases companies can use session recording tools:

  • Identifying mobile-specific issue that doesn’t exhibit on desktop
  • UX issue that occurs only on specific browsers and OS versions
  • Visualizing user click streams through heatmaps identifies where users spend more time and where they spend less time. This page-specific insight gives you to improve user experience and increase conversion rate.
  • Try out new features for a specific set of users and collect user data to analyze if the users like them or not.

What are the top website recording tools?

1. Fullstory

Fullstory offers session recording tools and other tools for heatmaps, A/B testing, etc. Using the data collected from Fullstory tools, you can perform both quantitative and qualitative analysis to improve your website visitors’ browsing experience. The easy-to-use UI helps you visually identify visitors’ pain points, using which you can uncover new opportunities to improve your website conversion rate. 

Fullstory offers a 14-day free trial, and you can register and test it for your website before making a total commitment.

2. Hotjar

Using Hotjar you can visualize user engagement on your website. It records all the user actions on your website using a recording tool and displays a visual heatmap of user clicks. Using the visual click data captured, you can identify which part of the website users engage the most and where they’re not engaging with your website.

Hotjar also offers tools for creating sales funnels and survey templates to analyze and understand your users browsing insights.

3. Mouseflow

Mouseflow tool records all the user clicks and has a Friction Score feature that shows which user session has issues. You can go to particular user playback instead of wasting time analyzing all the user sessions. You can create cohorts (groups) to combine similar user sessions and perform collective user behavior analysis on the session data. Like other tools, Mouseflow has tools for heatmaps, form analytics, and funnels. Combining all these tools with the user session data, you can gain 360 degrees of your user journey on your website.

Mouse flow offers a free tier that gives you life-long access to their tools. Using the free tier you can record and playback 500 user sessions for one website for a month of session recording storage. 

4. SiteRecording

SiteRecording tool captures all your website user journey and stores it for later playback. Its visual dashboard gives valuable behavioral data like which geography the users visited, what pages they visited, how long they stayed on the page, etc. Using the visual graphs, you can identify user patten, determine their interests, and makes you make decisions based on the actual data collected from the session recording tool.  

SiteRecording offers free registration for ten users, you can register and start using the tool immediately. 

5. Smartlook

Smartlook session capture and playback tool support both website and app; its advanced filtering capability allows you to move to crucial movements during playback of the user session. You can create filters based on various parameters like location, device, URL, etc. this feature helps reduce your playback viewing time.   

Smartlook supports free tire with a maximum of 1500 session recordings per month, using which you can test how the tool works before paying for the tool. 

6. Livesession

Livesession tool has features to capture user session recording and playback. Its heatmap feature allows you to view which part of the website users are spending most of their clicks and helps you to improve the efficiency of your website. Livesession also has a funnel feature using which you can identify where the users are dropping off from their website journey, using which you can increase the conversion rate.

Livesession has a free tier using which you can capture up to 1000 free sessions and test the tool.

7. Lucky orange

Lucky orange has tools, Dynamic Heatmaps, Session Recording, and Live Chat; you can capture and analyze user website vising behavior using these tools. The session playback tool allows you to adjust speed, skip idle time, etc., and view the trouble points quickly. The heatmap tool will enable you to study the clicks and scrolls of your website, and you can view each of your website elements’ performance. 

Lucky orange offers a 7-day free trial using which you can test the tool.

8. Crazyegg

Crazyegg uses session recording and visual report tools to understand user journeys.  Using the heatmap tool, you can see how your traffic sources and marketing channels are performing. You can also generate a report that shows the individual page performance by traffic sources and marketing channels.

Crazyegg offers 30-day free trial for you to test the tool before making the purchase.

Conclusion

All the above tools have almost identical features in their offerings. Most of the above session recording tools offer a free trial period, using which you can test it before making a decision. As a website owner, you can use these tools to identify website issues, optimize the performance and increase your visitor’s conversion rate.

 

Analytics to Business Value (ABV) – Agility in Digital Product Development

Digital-first companies are using a new strategy,  analytics to business value (ABV).  This method uses customer data to dramatically increase the speed of product development and cost optimization efficiency. It collects, analyzes, and creates business intelligence from customer data from various channels like websites, social media, voice transcripts, etc. By leveraging data and analytics, this technique creates more insight in a shorter timeframe, often within weeks rather than months at scale and across the product portfolio.

Use Case Driven Approach

In the ABV approach, organizations focus on solving a specific use case for the customers instead of focusing on complex technology to provide a solution for the use case. Though it looks simple, organizations often make the mistake of developing a technical solution without fully understanding the business problem to which they’re trying to find the answer.  For example, this approach may not add value by creating a cutting-edge technological solution because their competitors are using it. Companies must first understand what use case the data will solve and start from the use case rather than the technical solution. Without a well-defined use case-driven approach, many IT projects often disappoint by exceeding the original budget without providing any tangible value to the business.

The Use Case Driven Approach is only the beginning. Creating custom-tailored analyses becomes a one-time effort for business analysts working on a specific business problem. Analysts may spend several days curating and linking multiple data sets to answer a business problem. Due to a lack of resources, the curated data set remains with the analyst and is never used again. Next time for a similar situation, another analyst may go through the same process to curate the data. 

The use case-driven approach is the precursor to achieving analytic business value. Companies can incorporate the following steps to achieve agility in their digital product development.

Steps in achieving analytics to business value (ABV)

Following are the main steps in achieving ABV; combining these steps, organizations can achieve agility in their digital product development and create business value for their companies.

Customer-centric product design – Digital product optimization starts with identifying customer requirements; the organization should collect the correct data for the analytics and set measurable metrics to achieve the needs.  The speed of innovation and time to market to bring new products and services are reducing. As a result, innovative businesses have shifted to an iterative, agile product design and development process centered on creating a minimum viable MVP to bring digital-friendly products to market quickly. 

The ability to identify customer needs and design products and services accordingly has always been critical; traditionally, it has been a qualitative process based on marketing and sales teams’ experience. Today, however, there are methods for maximizing returns on customer research by utilizing the vast amount of customer data collected through website analytic tools. Leaders in all industries who are looking for robust methodologies and powerful digital analytical tools to improve customer value and product design must answer two fundamental questions:

How can I quickly get actionable insights from a dispersed customer base?

Consolidating Product portfolio – Organizations can optimize, retire and remove products that have less or no value to their customers.
Product portfolio standardization – Organizations must standardize their product portfolio, and business and IT groups must work together in portfolio standardization. 

Getting data from customers
Traditionally customer feedback is obtained through surveys, interviews, focus groups, etc. Nowadays, companies can get customer feedback through website visits, social media postings, callcenter transcripts, etc. Customers’ feedback has traditionally been acquired through a limited number of formal channels, such as surveys, interviews, and focus groups. Companies may now get a wealth of client feedback from various sources, including social media, website visits, and site reviews, as well as emails and call-center transcripts. This data contains a wealth of information for companies to find instant customer sentiment on their products. Understanding the data and creating tangible actions was a problem for companies until recently.

With the advent of new digital analytical tools and platforms, companies can get real-time customer sentiment; they can accurately find if a customer is happy or unhappy with their product and services. The analytical tools allow the companies to fix the issue in real-time and measure the customer sentiment to determine whether the problems have been corrected. For example, from the social media comments, companies can compare customer sentiment on their product category with their competitors on various measures like customer buying experience, feedback, price comparison, etc. Now the digital analytical tools and platforms allow companies to instantly get real-time feedback on their brands and their competitor brands.

How can I balance customer value and product cost?

Balancing customer value and product cost
By using customer sentiment data, companies start changing their product. Now they’ve measurable data and can identify what needs to be changed in their development, reducing cost and increasing customer satisfaction. They can go feature by feature to analyze the customer sentiment directly correlating to minimizing the product cost and maximizing the customer value. Traditionally it takes months, sometimes years, for companies to incorporate customer value into their products. Now with digital analytical tools, the entire process can be achieved within weeks. Companies can create an MVP, test it, get instant customer feedback, refine the MVP, and the cycle continues.

Consolidation and standardization of product portfolio
Consolidation and standardization are two challenges companies must address to gain agility in their product development. Consolidation of products directly affects external customers; instead of using multiple products, they may have to switch to a single product. Companies must make sure this doesn’t negatively affect their satisfaction. Product standardization affects the internal organization; business and IT should work together to reduce the product offerings and standardize the existing products with a standard interface. Achieving these two gives tremendous agility to companies in bringing new products with a customer focus. 

By combining the use case-driven approach and collecting customer data through digital analytic tools, companies can gain valuable analytics and competitive business value. They can transform their entire product portfolio to true agility. 

Top 5 Tools for Digital Marketing for 2022

Today, the Internet is saturated with all kinds of marketing tools to help teams effectively streamline their marketing processes. But the constant push to have the newest tools can lead your marketing arsenal to be cluttered and overwhelming. Marketing tools are here to help you, not the other way around. That’s why we wanted to provide you with a list of the best marketing tools online today.

1. Teamwork – Project Management

Teamwork is a top-tier project management software that, as the name suggests, has superior collaboration capabilities. In addition, it has strong organizational features that both you and your clients can take advantage of. While it does take some time to set up, the Teamwork team will provide you with robust support to get everyone acclimated. After that, you have the option of adding clients to Teamwork.
Teamwork facilitates easy collaboration by allowing project managers to set tasks and allowing the whole team to view current status updates on each project. Team members can work synchronously on the same project in real-time. It also integrates with a whole host of other tools, including HubSpot, Grows, and QuickBooks Online.
Teamwork also allows you to create and save templates, which can allow repetitive tasks to become frictionless and helps your team train new team members.

Tasks created by project managers can be subdivided into subtasks, allowing for larger projects to feel less overwhelming and providing clients with clarity on a project’s progress.

2. HubSpot – CRM

HubSpot is on our list because it’s the gold standard CRM for marketing, no matter your goals. With strong scalability, expansive integration with other tools, and variable pricing plans (the main features are free!), HubSpot is a highly effective and valuable tool.

HubSpot’s UI is incredibly accessible as well. After creating a free account and importing your contacts, Hubspot facilitates their categorization by current lead status and makes it easy for you to assign each lead to a member of your team. You can connect your inbox in a few easy steps to begin tracking company insights, deal values, and so on. HubSpot makes it easy to see the whole sales pipeline and allows you to view offline data, such as phone calls. It also has a number of extensions and tools for other Hubs, including Marketing, Sales, and Operations, and if needed, you can scale up to HubSpot’s even more robust premium features.

3. Mailshake – Link Building/Outreach

Mailshake is a great tool for your team to streamline their outreach workflow. Mailshake provides automated cold email outreach that is automatically personalized and automatically sends scheduled follow-ups, greatly simplifying any outreach campaign. It also provides analytic information, including monitoring opens, clicks, and replies to give a detailed picture of engagement. Mailshake also helps you identify the most successful parts of your campaign by tracking conversion rates and provides you with simple tools such as A/B testing to improve the success of your outreach over time.

Mailshake tool

After creating a template in the Mailshake dashboard, you can use their text replacement features to personalize each email. Once you upload a spreadsheet or CSV file that has personal contact information, the template will automatically populate each email with personalized information.

4. Ahrefs – Keyword Research/Rank Tracking

Ahrefs can be a centralized tool for your keyword research and SEO needs. Ahrefs is easy to use, but it’s also chock full of information, making it incredibly useful for tracking keywords. When searching for keywords, Ahrefs provides you with a plethora of valuable information, including traffic potential, search volumes, number of clicks, and ranking difficulty. It also provides information from over 171 countries and ten different search engines, which is especially important if your team is working to market internationally.

Ahrefs also provides you with thousands of suggestions for related keywords and allows you to figure out whether your page can rank for a targeted keyword while also ranking for multiple related keywords.
Ahrefs has a number of other robust tools, including Rank Tracker, which tracks your SEO ranking progress; site Explorer, which allows you to compare your performance with your competitors; and Site Audit, which allows crawling your site for any SEO issues.

5. Surfer – Content Optimization

Surfer is a content optimization tool that is very useful in directing you towards additional keywords. Like us, Surfer utilizes AI technology, or more specifically, Natural Language Processing (NLP) algorithms, to target the most common keywords in highly-ranked posts with your target keyword(s) and then provides NLP-generated suggestions for additional keywords to add.

Surfer also provides you with AI technology to optimize an article and content outlines, including paragraphs, headings, and image counts, based on top-performing pages. You can also audit your content with their content audit tool.

Curating your Team’s Marketing Tool Arsenal

Tools are useful for improving your team’s efficiency and effectiveness, but with the sheer number of tools out there, it’s easy to jump onto the latest marketing tool trend and quickly rack up thousands of dollars in software purchases. So take a look at your current arsenal. Many of the tools we’ve listed here today are robust enough to do the work of several tools at once, so be mindful and conscientious about the tools you choose to purchase.

How to Create a Marketing Funnel

A marketing/sales funnel, or Funnel, represents a user’s journey on a website or App. The Funnel represents each touchpoint from the initial stage when a user learns about your website to the actual conversion, where they decide to buy an item or subscribe to your service. It can extend through the entire life journey of the customer.
In this article, we will explore the process of creating a funnel, optimizing it, and managing to get the most value from it.

What is a Funnel?

Every company wants website visitors to arrive at the landing page and engage with their product or service offerings. They want their user journey to follow something like the following:

  • Learn about your brand/search for your product or service
  • Land at your website
  • Browse through your website
  • Make a purchase or subscribe to your service
  • Visit later and buy more

These steps described above are the stages that make up the Funnel. But it’s called a funnel because the number of people present at the first step is high and at the last step is low, which creates the shape of a funnel. The Funnel is also called the Marketing or Sales Funnel based on the intent; the marketing funnel is used to attract the visitors with a reason to buy, and the sales funnel is used to draw the visitors from the Marketing funnel to buy once and then continuously many times.

Stages of a Funnel
The entire marketing funnel works as a unified process. Every section needs to work perfectly for the user journey to be successful. Many things can reduce friction in the Funnel. For example:
Awareness: In this stage where potential customers are made aware of the brand and its offering through strategic content and marketing strategies that aim to educate the customers, capture their engagement and lead them to the website
Consideration: Once a user enters the website, the next step is to get them to start skimming, comprehend what they are looking for, provide relevant offers to leverage the power of the crowd through social proof, and encourage them to make a purchase.

sales funnel

Conversion: This is the ultimate goal of every website – the action that every brand seeks from the user. In this stage, you fine-tune the journey that leads to a seamless “purchase” experience. Considering the increasing drop-off rates along the Funnel, the number of users who make it to this stage will be considerably lesser than the number who started at the beginning. Therefore, it becomes crucial that companies achieve high retention at this funnel stage.
Note that users drop off along the path of the Funnel from top to bottom; the goal is to minimize the drop.
Loyalty: A loyalty program with frequent discounts, email interactions, and social media maintains customers.
Advocacy: Open-minded customers in the loyalty program support your future marketing funnels and recommend their friends in your offerings.

Marketing Funnel Example
Let’s take the case of an online eCommerce company that deals with customized women’s accessories and regular women’s products. Their market study has indicated that most potential customers are first-time customers who use social media platforms to achieve insights, share reviews, and shop on the eCommerce site.
Having determined the target audiences, the company proceeds to run Facebook and Google Advertisements to drive first-time visitors to its eCommerce website. With potential buyers arriving at the website, the perfect situation for the company would be to have first-time visitors signup and move along the Funnel. This way, first-time visitors turn into leads continuing their journey in the Funnel.
Now the nurturing step, over the next few days, the company sends out informative emails to these leads, educating and informing them about its products offerings and discounts. At this stage, the company may also add specific incentives, such as first-purchase coupons, subscription offers, etc., to get the prospects to convert and continue their journey in the Funnel.
Conversion is critical for businesses. Nearly seven percent of customers leave after their first purchase. The average return rate for first-time visitors is less than twenty percent, and repeat customers spend twenty percent more than first-time customers.
This is why companies need to repeat the process and continuously engage with the customers to make them purchase again from their website. Ongoing engagement is a vital part of the Funnel, yet one that is frequently overlooked.

How to Build an Effective Funnel

Now that we have the funnel basics, let’s focus on building an effective funnel. But before we do, let’s discuss each step of the Funnel in detail:
As we’ve seen earlier, the marketing funnel consists of 3 broad stages- awareness, Consideration, and conversion. These stages serve three specific purposes, namely-
• Lead Generation [Awareness]
• Lead nurturing [Consideration]
• Sales [Conversion]

funnel building steps

Lead Generation

This funnel step is all about the marketing campaigns run by companies and customer research that make up the awareness stage. 

invest in lead generation

To engage with the consumers at this step of the Funnel, companies need to invest in marketing campaigns, writing articles, webinars, social media, and Google search ads to maximize their outreach and capture the attention of their target audience.
Lead generation should be an ongoing process as it entails a long cycle and takes time to see the result. Companies that invest in understanding their target leads at this stage can show relevant and engaging lead-generation content. These days, companies can use many customer journey builder tools to engage, nurture and convert potential leads along the Funnel to the next step in the Funnel.

Lead Nurturing

Lead generation must be followed by lead nurturing. In this step, customers are introduced to the company’s offerings and product positions through engaging content and discounts.
Emails, targeted content, newsletters, etc., are common ways companies nurture their leads and move them along the Funnel and on their path to conversion. It is also crucial for companies to remember that at this stage, potential customers are looking for more information about the company’s products and services.
In this step, companies must educate and advise the potential customers about the products through free trials, discounts, etc., using automated email campaigns and targeted ads.

Sales (Conversion)

At this step of the Funnel, the potential customers are ready to receive marketing and sales info that advocate the quality of a company’s products.
In this step, you can do demos, trial offers, free samples, etc., to build trust through sales and relationship management.
The metric in this step will be either the purchase or the “conversion” of the user from being a ‘prospect’ into a paying customer at the end of the Funnel. A user’s journey through the Funnel is long and often time-consuming, but given the right nudge at each step, companies will undoubtedly be able to improve the conversion rates as the funnel winds down.

Practical guide in building a Funnel

How to build a marketing funnel? In this section, we will explore creating a Funnel.
Identify your target customers
The first step in building a Funnel is identifying your target customers. You can use tools like Google Analytics to place your website visitors and perform social media market research to know your potential customers for your products or services.
Get target customers’ attention
Even the most awesome Funnel won’t fetch you the results unless you can attract your target customers to enter the Funnel. Your Funnel can only begin functioning when the target customers enter it. The process starts with getting the right content in front of your target customers. Write SEO-optimized content like articles, blogs, case studies, etc. Use Google paid and Facebook ads to get your customers to read your content and grab their interest and attention.
Get target customers to visit your landing page
Now that target customers have started engaging with your content, and you have their engagement, it makes sense to lead them somewhere, correct? You would need your target customers to reach your landing page, which explains your product or service offerings. The main aim of this page should be to guide them to the next step in your Funnel, a clear call-to-action that makes them download your article, case study, or sign for a newsletter by giving their email.
Get started with nurturing campaigns
Now that you have the target customers’ email, your next step is to send marketing emails with discounts and coupons. However, make sure not to flood them with constant emails. Keep the frequency of your email campaign just what’s needed to stay on top of their minds, not bombard them with emails. Familiarizing, promoting, convincing, and making an offer they can’t refuse is part of the “lead nurturing” step. At the end of this nurture period, you should have encouraged your potential customers to convert and make a purchase decision.
Start the Funnel building process
To get new customers, convert and grow your customer base, it is essential to retain the new customers. The funnel steps contain both customer acquisition and customer retention. Continue engaging with your existing customers, encourage trust, build loyalty and keep them interested in your growth as they are a part of overall funnel success.
What makes you apart from your competitor?
For your customers, your company is just one of the names in a highly competitive list of choices they have. Effective funnels can help you offer experiences that apply to what your customers are looking for from your site.
Why should you optimize your Funnel?
Optimizing your Funnel will require you to position yourself as a brand that stands out from your competitors, and you want the customers to choose your products or services over your competitors. Your main job is to draw your customers’ attention to the value of your offering. Don’t concentrate on pushing your customers to buy your offering; that should happen organically through your Funnel.
It may seem like hard work and a lengthy process, but your competitors are doing it, and it is the only way to persist in today’s competitive digital world. When you make an effort to build a funnel that resounds with what you stand for and what your customers are pursuing, you will find that it works perfectly.
An optimized funnel keeps your brand on the top of your customers’ minds. Combined with relevant content, you can build trust and ensure your customers don’t need to look at your competitors.
How do you Measure the Effectiveness of Your Funnel?
The most significant measure of success of your Funnel is how frequently you refine it as you understand your customers. As you learn more about your target customers, their behavior, and intent, and your brand grows and diversifies, refining your Funnel becomes critical. This refinement of the Funnel is what will bring success to you.
For KPI, an accepted benchmark is a healthy conversion rate. Tracking these metrics can help you determine what might not be working for your brand and tweak future engagements to fit completely. E.g., How many people created an account by clicking on your Facebook or Google ad?
Another practical way to measure the success of your Funnel is to customize customer engagement at each stage of the Funnel:
By paying attention to each step of the funnel, you will be surprised to see how the Funnel can be refined to get maximum conversion. You are maintaining your Funnel optimized and ready to engage in a continuous process. You must constantly monitor and adjust your Funnel.
Are there any limitations to Optimizing the Funnel?
Not really. You can create and build multiple funnels to engage your customers at various touchpoints of their journey and engage them for specific conditions. You can optimize your Funnel to any number of scenarios.
In addition to the scenarios mentioned above, customers may leave your website as the product they are searching for may be out of inventory. You can re-engage with these customers once the product is back on your list. For price-sensitive customers, you can send them emails with discount offers to nudge them into buying your product and increase the conversion rate of your Funnel.
How Can your employees help in optimizing your Funnel?
When we look at the Funnel, we often visualize a unidirectional flow, where new customers sign up and eventually make a purchase. But funnel optimization can be applied in different scenarios, not necessarily limited to converting a potential customer into actual customers.
Optimizing the Funnel has multiple advantages for the company, from improving Average Order Value (AOV) to optimizing the Customer Lifetime Value, Average Revenue Per User (ARPU), and reducing customer churn.
Let’s explore a few use-case scenarios where you can optimize your Funnel and deliver an improved customer experience while achieving optimum results.

Category Funnel

E-commerce companies often find that users show more than a general interest in a product. They do so by browsing various categories of the product. However, this doesn’t always end in conversion, as they leave the page without making a purchase.
Marketers can take clues from such customer behavior. The fact that they are browsing the product across product categories means this user segment is more likely to purchase than others. So you should pay close attention to this customer segment.
Here is the way your employees can Help

 

sales funnel category
funnel category

For the more likely-to-purchase customer category, your employees can help engage with them and nudge them into making a buying decision. Using email campaigns, businesses can send the right email messages, enabling them to convert. They can also be addressed on a one-on-one basis with Messenger tools that can be used to deliver high-value, relevant and time-sensitive information to customers on their mobile apps.

Onboarding your Funnel

Storage space is not a constraint on smartphones today. Often, customers download applications on their mobile and then forget about them. Say you are an online retail business. It’s a crowded market, and users often download several online retail apps but end up not using them. How do companies motivate these customers to start using their apps? One approach is to send an email reminding the customer to use the App to order products to get discounts. Another method is periodically sending notifications (without overwhelming) to the customer’s mobile about the deal on your products.

New User Registration Funnel

A challenge for many marketers is turning new visitors who arrive at your landing page into leads you can nurture and convert into paying customers. But this is not an easy process; not everyone who lands on your website is looking to make a purchase or give out their email.
Remember, most of these visitors are in the Funnel’s consideration stage and are doing their research before committing to your product or service. As a marketer, the value-add you provide to visitors at this stage is to identify their intent and offer relevant and personalized content.
Following are some of the techniques you can use to convert a new visitors to register on your website
• Once a visitor lands on your website, you can engage them to register using personalized lead generation content.
• For that visitor who does not register on the first visit, you can help follow up with a personalized offer
• You can further engage visitors who still don’t sign up by providing a discount membership card.
This strategy is based on the website traffic analytic tools to identify the visitor in various steps in your Funnel.

Cart Abandonment Funnel

Another pertinent example where funnel optimization can yield excellent conversion is while dealing with “cart abandonment.” Every year cart abandonment costs businesses multiple billion dollars in lost revenue.
The constant challenge for marketers is reducing car abandonment rates, as this is where many related marketing metrics can also be optimized, like Average Order Value (AOV).
How can you manage cart abandonment?
When customers abandon their cart and exit a website, it is a lost opportunity for your company, a highly undesirable outcome for you. However, you can send targeted emails giving discounts on the items added to the shopping cart to nudge the customers into making the purchase. Also, identify your checkout process for issues, fix them, and simply the entire checkout process.

Returning Visitor Funnel

A visitor checks into your website and leaves without making a purchase. How do businesses engage with the visitor so the visitor purchases the subsequent visit?

How to fix Returning User Funnel

Businesses looking for higher conversions is to understand their target customers based on customer segmentation. You can use third-party tools to create visitor segmentation based on visitor behavioral patterns, response to marketing emails, purchasing trends, geolocation, historical data, etc. Once you have the visitor segmentation in place, identify which segment the returning customer falls under and perform a targeted marketing campaign pertinent to the visitor segmentation.