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Industry Innovation

Assessing On-Line and Off-Line Consumer Behavior

Creating a Predictive Consumption Model

graphic displaying customer purchasing on and off line

s a c-store operator, it would be nice if you could predict future customer behavior. That way, you would know how much inventory to purchase and how many employees you need for daily operations.

Well, now you can do all that using predictive analysis. Simply put, it means compiling statistical data to make predictions concerning consumer behavior. But it goes much deeper than knowing what products are selling and which ones are not.

These are just a few of the benefits of utilizing a predictive consumption model for your c-store:

  • Know which times of the day consumers are buying
  • Make more accurate seasonal predictions
  • Discover better ways to allocate staff
  • Boost sales long-term
  • Predict vital inventory requirements

Here, we go over the basics of predictive analytics and its most crucial components. Then, we will show you how to obtain the necessary data. Last, we list the steps for getting started with predictive analysis tools that are readily available.

The Basics of Predictive Analytics

At its core, predictive analysis utilizes machine learning, data mining, and predictive modeling to analyze customer behavior. Predictive algorithms capture patterns that are later used to score each consumer.

It is a forward-looking technology that uses historical data to predict future events. Sometimes referred to as big data, it employs computer algorithms, artificial intelligence, and predictive modeling. Retail experts sum it up this way:

In summary, big data in retailing today is much more than more rows (customers). When one takes the multiplicity of People x Products x Time x Location x Channel data, this is big data. Retailers that have the ability to link all of these data together are ones that will be able to not only enact more targeted strategies, but also measure their effects more precisely.

C-store operators use this information to predict sales growth and market trends. The benefits are obvious:

  • Increase revenue
  • Improve ROI
  • Remain ahead of competitors
  • Know when to course-correct

Predictive analysis works well for all product classifications, including homogeneous and heterogeneous products. Furthermore, it does not matter what you sell since the end goal is essentially the same.

What Are the Components of a Predictive Model?

Predictive consumption analytics is a complex field of data science. Therefore, it is helpful to break it down into categories or components.

Consumer Behavior

By analyzing the buying habits of your customers, you can identify your marketing success rate as well as discover which communications channels are working the best. You can also tailor your products to meet the growing demand within a particular category.

Customer Journey

Customer journey analytics explores the buying process from start to finish. It takes a comprehensive look at the entire relationship between the business and the consumer.

However, it does not end with the purchase. Instead, it lends valuable insight so you can plan your next cross-sell, upsell, and resell promotions.

Operations and Logistics

Predictive analysis allows c-store managers to order the correct amount of products based on historical data. It analyzes fluctuating demand and considers economic trends so you can proactively manage your inventory. The result is increased profit margins due to waste reduction.

Competitive Pricing

It is possible to attain valuable data concerning competitor pricing using predictive analytics. Most of the predictive software packages available now have this feature. It will even make suggestions based on correlated data as to what price point is best for each product.

How To Obtain Data

The tricky part for most c-store operators when utilizing analytics tools is obtaining the data. Sure, you have your rewards card in place, but technologies are changing rapidly. The latest advancement in loyalty programming involves QR codes and mobile apps.

Mobile Apps

Traditional plastic loyalty cards cannot readily interface with predictive software packages, leaving you out in the cold. The good news is that by offering mobile payment transactions, you can capture all the data you need on every customer. This includes those who walk through your doors or drivers purchasing fuel at the pump.

Point-of-Sale (POS)

Your POS system is an excellent asset for collecting customer data. For example, it retrieves payment details and personal information such as zip codes and payment history. Your customer could also opt-in to receive digital receipts, which will help you build an extensive e-mail list.


In-store customer surveys are another great way to capture vital data. For example, you could use receipts to direct your customer to a website or have them fill out an in-store survey form. However, offering some type of incentive is always best to achieve the maximum response.

Online Customer Reviews

Customer feedback is crucial to the success of any business operating in today's culture. Use it to compile information about customer sentiment.

The primary review sites to check daily are:

  • Google reviews
  • Yelp
  • TripAdvisor
  • Facebook
  • Better Business Bureau
  • Yellowpages
  • Manta

While it may be difficult to read negative reviews, it can also be a great opportunity to connect with customers. Instead of ignoring them, it is best to acknowledge the feedback and reply honestly. This builds customer trust and shows that there is a human face behind the company.

Social Media

Retail businesses utilize their social media campaigns to collect customer data. Here are some of the sites to consider:

  • Facebook
  • Twitter
  • Instagram
  • TikTok
  • Pinterest

There are many more, but these are the main ones. Most have built-in analytics tools and can provide your c-store with a suitable business profile.

Getting Started

Predicting consumer behavior is not exclusive to e-commerce businesses. Many companies offer analytics software packages to onsite retailers as well. Included are:

  • IBM
  • Oracle
  • Numerator Insights
  • Alloy
  • TMAS

This is certainly not an exhaustive list, but it will get you started on making comparisons. When shopping for an analytics software program for your c-store operation, it is best to have these features:

  • Omnichannel data collection
  • Full integration with social media sites
  • Simple user interface (dashboard)
  • Downloadable reports
  • Solid customer service

Your software should also provide periodic updates to adapt to an ever-changing business environment. That means being agile enough to perform well, no matter how much demand you put on it.

Key Takeaways

The field of predictive analytics is not just for the big tech giants anymore. Now, with newly-developed software, you can receive the same vital data for your C-store operation.

Applying predictive analysis to your c-store will provide you, at the very least, with a snap-shot of what your customers are thinking and doing. This information can be an invaluable tool going forward and may put you well in front of the competition when used correctly.


Bradlow, E. T., Gangwar, M., Kopalle, P., & Voleti, S. (2016). The role of big data and predictive analysis in retailing. Dartmouth College Journal of Retailing, 1(1), 6.

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