7 Steps to Building a Successful Customer Behavior Analytics Model

7 Steps to Building a Successful Customer Behavior Analytics Model

Having a comprehensive customer behavior analytics model is vital to ecommerce companies. Studies show that companies that use customer behavior information to their advantage outperform competitors by 85 percent in sales growth. Additionally, estimates show those who use behavior analytics beating competitors by an extra 25 percent in gross margins. The current influx of data available to measure customer behavior patterns is making it easier for ecommerce businesses to use analytics in the following ways:

  • Support core sales and marketing goals
  • Increase customer satisfaction rates
  • Improve product/service criteria
  • Optimize marketing channels
  • Make insightful improvements to overall sales strategies
  • Increase customer loyalty

Developing a customer behavior analytics model is vital to retail success for a variety of reasons, and ecommerce companies shouldn’t be without one. Read on to discover seven key ways to build the right customer behavior analytics model for your ecommerce business.

1. Use Detailed Data

As previously mentioned, there is an influx of big data in ecommerce businesses that help monitor customer behavior. Present analytics platforms can delve even deeper into consumer patterns, and tracking tools have become far more accurate. Estimates for the 2020 fiscal year suggest that ecommerce sales will pass over 4 trillion, and companies with a pulse on consumer behavior will take a larger chunk of those sales. The following forms of data can help retailers build a broad customer behavior analytics model:

  • Order tracking: includes buying patterns, most purchased products, and reordering habits
  • Consumer engagement: helps gauge what features, products, and information matters most to consumers
  • Changes in order patterns: helps businesses anticipate possible upcoming ordering changes that an online consumer may go through
  • Conversion rates: how many consumers convert from visitation to purchasing from a brand
  • Retention rates: how many consumers remain with a brand long-term
  • User feedback: customer satisfaction ratings, reviews, comments, and likes

The ultimate goal of using customer behavior analytics in retail is to get actionable insight into what consumers are looking for. Criteria for that can vary by company, which means the data needed can also vary; the list above gives forms of data that are useful to every ecommerce business. This is just a short list of ideas, and there are many forms of data that help ecommerce businesses build relevant models for customer behavior analytics. When determining what data to add, keep all major business specific KPIs in mind to get the best possible analysis of consumer behavior.

2. Throw Out the RFM Model

Use a life-time value (LTV) model instead of a recency, frequency, and monetary (RFM) model. Ecommerce businesses in particular benefit from using LTV over RFM. Digital tracking and an increase in the accuracy of analytics platforms has made RFM models a poor choice for customer behavior analytics. The reason they aren’t a good fit is because the majority of the key indicators for that model are based on immediate sales. They also primarily focus on those who spend the most money, and they lack any tangible insight into retaining new customers long-term. LTV models can provide actionable insight to take for individual consumers, target their long-term value to the ecommerce business, and select appropriate marketing for them.
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3. Stop Making Assumptions Based on Age, Demographics, or Finances

There are 35-year-old people who watch cartoons, and believe it or not, that’s an excellent point of reference for this segment. Customer behavior isn’t measured by simply lumping age groups, demographics, or income levels into tidy piles and assuming what they will or will not respond to. Making assumptions about customer behavior patterns is not conducive to developing a successful or accurate customer behavior analytics model. The goal is to start thinking of customers as individuals, not groups. A 70-year-old can buy hiking gear and a 20-year-old may purchase knitting needles. To get the best possible results, find out what individual consumers want by basing analytics on real data and not assumptions. In short, don’t stereotype.

4. Use Predictive Analysis

Predictive analysis is an excellent way to draw in potential customers, keep existing customers, and anticipate future needs in order to suggest relevant products. Predictive analytics drive the following ecommerce metrics:

  • Customer acquisition: helps by tracking the consumer’s journey from the initial site visit to checkout to personalize their experience
  • Customer retention: helps by anticipating problem areas to repair based on data such as customer feedback or drop rates
  • Customer growth: helps retailers create calls to action based on ordering patterns during specific periods

Personalized advertising, suggestions, and promotions can all be tailored to customers using data obtained from predictive analysis. Using predictive analytics to understand the behavior of customers is another way to use big data in ecommerce to make changes that improve retention.

5. Know Your Goal and Create Steps to Reach It

Building a comprehensive customer behavior analytics model means knowing what goals your ecommerce company wants to achieve. There are many steps to building customer behavior analytics that companies can take advantage of to reach goals, including :

  • Set analytics goals and KPI’s, and track them
  • Determine critical paths, then break them up to get the most data
  • Set user properties to receive data on customers using your site
  • Continually monitor and adapt analytics models based on consumer practices and new data
  • Measure success of new products to determine their impact on sales
  • Use funnel analysis methods

Having specific goals in mind while developing customer behavior analytics helps ecommerce businesses make marketing and advertising decisions and changes based on reliable data.

6. Incorporate Funnel Analysis

Funnel analysis is particularly useful for determining abandonment rates through each stage of the checkout process. They also help establish the set of steps consumers must go through to reach any specific outcome on a website. Funnel analysis helps ecommerce organizations visualize data by showing drop off points. For this reason, these types of analytics are ideal for verifying drop rates, tracking site abandonment, and showing weaknesses that exist in each stage of the process. Funneling is also an excellent tool for ascertaining why conversions were unsuccessful.

7. Search Out Customer Access Points

Are you getting the most customer engagement from external or internal links? Are they accessing the site through social media posts or click ads? It’s important to pinpoint consumer access points for your brand and track behavior across all points. This type of behavioral analysis helps businesses target areas that produce the most clicks and conversions. That way, retailers can position ads appropriately to maximize their potential draw. Being able to allocate resources properly is one of the key benefits of analyzing consumer behaviors. For customer behavior analytics models to be successful, they have to include data from all areas of the business that consumers have access to. Access areas to include during the process of developing a customer behavior analytics model include:

  • Primary website: main ecommerce site
  • Apps: downloads, uninstalls, in-app purchases, and feedback
  • Social media: Facebook®, Twitter®, Instagram®, etc.
  • Click ads: any advertisement that provides a direct link to primary site
  • External links: links on other sites that direct users to primary site or other company resource

Metrics measurements should span across any location that gives users access to your brand, or they are not truly accurate. Using data from websites alone can give a lopsided view of customer behavior, because not everyone will access your product through the main site. It all circles back to the importance of analyzing customer behavior on an individual level.

The insight gained into the personal habits of customers is crucial to recommendation searches, email campaigns, and product suggestions. Do you have any insights into creating a successful customer behavior analytics model? Comment so readers can add them to the information they gained from this post. If you still need guidance on how meet your ecommerce consumer goals, contact eZdia where our professionals will be more than happy to help you succeed.

Author: Kristin Ann Hassel
Email: kristin.ann.hassel@gmail.com
Linkedin: https://www.linkedin.com/in/kristin-hassel-8651a3157

Why Every Ecommerce Business Needs a Predictive Customer Behavior Analytics Model

Why Every Ecommerce Business Needs a Predictive Customer Behavior Analytics Model

The availability of consumer data has made it easier for retailers to direct ad campaigns and product recommendations to individual consumers and specific behavioral groups. Customer behavior data can help ecommerce businesses personalize promotional offers, drive conversions through behavioral pattern recognition, and find areas that are performing well and ones that need a tune-up. Every ecommerce business should have a personalized, predictive customer behavior analysis (CBA) model.

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Benefits of a Solid CBA Model for Ecommerce

Insights from CBA can help ecommerce businesses personalize advertising, replicate long-term customers, reduce acquisition costs, and increase leads and conversions. Targeting email promotions, recommendations, and campaigns to individual consumers offers personalized solutions that drive further sales. Satisfied customers are more likely to recommend the brand to friends and relatives, or share products on social media or through email. Using big data analysis, ecommerce businesses can find consumers with behavior similar to their best customers. Etailers can replicate the marketing efforts made to obtain and keep lifetime customers to create leads and potentially gain conversions. Knowing what consumers are looking for and predicting future behavior that may influence purchases means less guesswork when creating marketing campaigns. Determining like behaviors and gleaning valuable insights when creating successful campaigns the first time around helps reduce the amount of money spent trying to acquire customers. Having a predictive CBA model in place assists an ecommerce business through:

  • Better personalization: being able to create marketing directed at customers as individuals
  • Driving relevant content: add content based on popular content areas, topics, or consumer feedback from a variety of platforms
  • Design ICPs: big data analysis helps create specific profiles through the accumulation of digital information using CBA strategies
  • Predicting future behavior: tracking and analyzing individual data can help ecommerce businesses predict the next step in a customer’s purchase evolution
  • Finding best marketing practices: gathering customer behavior data to see what strategies are working and what practices need an overhaul
  • Tracking the consumer journey: tracking from discovery to ordering and beyond, then using data to drive recommendation engines and direct advertising campaigns
  • Adjusting analytics models and data mining strategies: help determine where data mining strategies are successful and what type of customer information gathering works but that still maintains brand trust

Customer behavioral analysis is becoming extremely popular in ecommerce. An estimated 69 percent of ecommerce businesses uses predictive CBA for acquisition, growth, and retention rates. By focusing on individuals or specific behavior groups, retailers can increase consumer satisfaction and turn visitors into repeat customers.

Types of Customer Behavior Analytics Models to Choose From

Customer behavior models are meant to answer set questions based on customer data analysis. Up until recently, the RFM (recency, frequency, monetary) model was considered the most effective method for CBA models. The primary goal of RFM is to focus on sales, and ecommerce specialists have realized this model of analyzing customer behavior has become obsolete. With the increase in data available and consumers’ need for a personal connection to brands, models have evolved to include those that focus more on consumer satisfaction than sales. The idea behind the shift is that sales will follow satisfaction, making the consumer the most important factor. There are three main CBA models that work better for ecommerce than RFM:

1. Customer Journey Analytics

Analyze data from all channels the customer interacts with throughout the entirety of their experience with your brand. This model helps an ecommerce business determine what drives consumer purchase behavior. Track four major steps in the purchase path to get a full picture of consumers’ journeys:

  • Awareness: influences and triggers
  • Consideration: product and ecommerce brand research
  • Conversion: where and when purchase decision was made, what step in the journey
  • Evaluation: experience, feedback left, satisfaction rating, review, any user-generated feedback

Gathering data from each step of an individual customer’s journey can aid ecommerce businesses in developing or adjusting personalized marketing strategies.

2. Behavior Segmentation

The behavior segmentation model entails collecting data about the actions customer take, based on behavior patterns during the purchase-decision process. The data is used to help classify behavior groups and develop engagement strategies. Behavior classification is a better way to create groups of like-minded consumers without relying on age or geographic locations.

3. LTV

Modeling customer behavior analytics includes collecting customer’s data to determine their lifetime value (LTV) to an ecommerce business to obtain actionable insight on how to maintain customers with like behaviors long-term. Unlike RFM, which focuses primarily on immediate sales value gained by conversions, LTV is centered on the value of a consumer through their entire time with a brand.

Ways to Use Customer Behavior Analytics to Increase Conversions

The three most important attributes consumers consider when deciding where to shop online are best price, preferred website, and best delivery methods. An ecommerce site is more likely to be preferred if they are in tune with consumer behavior and display knowledge about consumers as individuals.

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One of the primary benefits of using predictive CBA is the opportunity to transform data insight into conversions for your ecommerce business. There are three key ways predictive analytics models can make the largest impact on ecommerce conversion rates:

  1. Increase customer satisfaction ratings: Predictive CB analysis obtains data from designated areas that can be used to increase customers’ personal experiences. The more satisfied consumers are, the more likely they are to recommend the brand to friends and relatives through email, social media, or word-of-mouth.
  2. Lead-to-marketing reconstruction: Predictive analytics can determine on which areas you need to focus the advertising budget, based on consumer purchase data and user-generated feedback. Consumer data can indicate how advertising campaigns have been successful and areas that need to be restructured or omitted to achieve better results. This provides an opportunity for ecommerce businesses to increase sales through personalized and behavioral group marketing campaigns.
  3. Attract potential partners: Positive statistics from data analysis methods can be leveraged to promote your brand on social media platforms, advertisements, and even turned into badges that can be placed on your website. A successful CBA model displays a knowledge of industry practices for optimizing success and makes your brand a trusted source of credible information on consumer behavior analytics methods.

Having partners increases brand visibility through recommendations for a product on their blogs, advertising, or recommendation engines. CBA is useful in the collection of data pertaining to what platforms draw the most user-generated feedback, which is vital for lead-to-marketing reconstruction. Knowing which one will work best for your company takes some research into current goals and possibly past data sets for customer satisfaction.

Customizing a Comprehensive CBA Model

Data points for each ecommerce business need to be customized to their end goals. The type of customer data they need depends on what type of goal they wish to accomplish by collecting customer behavioral data. For instance, if you’re trying to create a recommendation algorithm for an automatic feature based on consumer viewing history, you won’t need age or income information. One working example of this is Amazon’s recommendation algorithm, which uses the following data sets:

  • Purchase history
  • Shopping cart items
  • Rated and liked products
  • Views and purchases

Developing a list of areas vital to the goal they wish to accomplish helps ecommerce businesses develop a comprehensive CBA model that weeds out irrelevant data.

1. Include Unstructured Data

Using unstructured data allows etailers to gain insight from traditional forms of customer information, such as test data. These are available wherever user-generated feedback is found on social media, in comments, reviews, and other areas where consumers have direct interaction with a brand. Just because it isn’t necessarily big data, doesn’t mean it isn’t important data.

2. Know Your Goal

All the data in the world is useless if you don’t know what you’re looking for. Before developing an analytics strategy, ecommerce organizations should ask and answer the following questions:

  • What does the company plan to accomplish with customer behavioral data?
  • What metrics matter to the goal?
  • What are specific areas of concern?

Not having a clear view of what consumer data gathering is meant to accomplish but developing a strategy for behavioral data collection anyway is an exercise in futility. The influx of data alone just causes confusion. Determine motives. For example, is the company’s primary concern cart drop-off, conversion rates, or poor advertising campaign results? Obtaining clarity prior to the implementation of a CBA model saves time and supplies retailers with accurate data sets that apply to a specific goal.

3. Focus on Relevant Data Sources

Only use data that focuses directly on your end goal. Each data collection effort should center on resolving a very specific set of issues or accomplishing a business goal. Focusing on specific criteria provides ecommerce businesses with more reliable data results.

4. Sources of Data to Include

There are three primary ways to collect customer behavior data, and all three data areas must be considered while developing a comprehensive CBA model.

  1. Direct contact: phone, email, surveys, user-generated feedback, etc.
  2. Digital tracking: collecting data on behavior patterns such as purchase history, reordering, view habits, shares, etc.
  3. Competitor strategies: tracking competitor success with data collection methods, and adopting relevant methods.

With any form of digital data collection effort, and some direct efforts, software generally sorts results and breaks them into understandable pieces of data. This way, retailers can gain actionable insight faster and negate human error. Without data-sorting software applications, piecing through vast amounts of digital data could take months.

5. Build a Customized CBA Model

Customize your CBA model as much as possible to gather actionable insight into consumer behavior, what drives purchases, or why customers don’t follow through. Design a CBA model that uses analytics methods that are relevant to your brand. For a CBA model to be successful, it needs to fit your specific goal and provide accurate real-time data that applies to that goal. Using a cookie-cutter version of CBA isn’t going to produce tailored results, but there are some steps that should be included in every CBA build. In our 7 Steps to Building a Successful Customer Behavior Model, the primary steps to include during the build of a CBA model are broken down. The steps include tools like predictive and funnel analysis methods.

What Are Ecommerce Businesses Presently Using CBA For?

The level of actionable insight received from the end results of CBA helps retailers keep customers that generally drop off prior to conversion. In fact, 63 percent of ecommerce businesses use the data to increase customer satisfaction rates, while 46 percent use it to elevate loyalty in existing customers. One of the most popular ways to use predictive analytics rests in recommendation engines like the one created by Amazon. The majority of Netflix viewer activity is driven by their recommendation service, and it’s estimated it saves the company $1 billion per year by reducing turnover rates. A key benefit of CBA is customization to individual consumers, which makes it crucial to ecommerce success. One study shows that 52 percent of consumers often switch brands when advertisements are not personalized to their habits. The possibilities for data-driven success are endless using CBA, especially when combined with other customer behavior data analysis methods and AI technology.

What information on CBA did you find most helpful? We’d love to hear how CBA has changed your ecommerce company for the better and any tips you might have about creating a successful model. Want help reaching your goal? Please contact eZdia to learn how we can assist. We strive to offer services that help ecommerce businesses reach their full potential. This is done by developing engaging content, analyzing and developing data for algorithms, managing content, and providing other valuable ecommerce solutions.

Author: Kristin Ann Hassel
Email: kristin.ann.hassel@gmail.com
Linkedin: https://www.linkedin.com/in/kristin-hassel-8651a3157