Digital Technology, Marketing, Thought Leadership

Predicting Consumer Behavior in the Retail Industry

Digital Transformation – Riding the Wave

Here is a not-so-rare scenario in retail. A sales promotion campaign has run for 3 years with loads of success, but then it fails in the 4th year. In most cases, there will be some amount of introspection to see what has changed in the company, and why the performance targets were not achieved.

In most cases, the change in consumer behavior is not caused by the company’s branding efforts, but rather by external factors such as convergence of digital channels, analytics, changing customer preferences and most importantly the competition.

Allowances can be made for small changes in consumer behavior through past sales analysis and forecasting and by effective prediction of consumer demand. As rightly said by Steve Jobs, “customers don't know what they want until we've shown them.” Retailers, must know what the customer wants, before the customer can know themselves.

Unlock your 30 min Complimentary Consulting Session

Ways to predict consumer behavior

Here are 5 methods that companies can use to predict consumer behavior:

1. Listen to your customer

After becoming the CEO of the ailing Procter & Gamble in 2000, A.G. Lafley had a simple mantra to revive his company, which was “The consumer is the boss.” Reiterating this mantra to his employees, Lafley urged them to listen to what the consumers were saying and what they wanted from the products, especially when they are unable to articulate their needs. P&G was able to drive their business decisions, based on a complete understanding of the customer wants.

In another case in 2009, the makers of the Wrangler and Lee jeans generated an additional $100 million in revenue, simply by changing the size labelling of their jeans and promoting a campaign to help women find the right fit irrespective of their body size.

2. Improving sales forecasts

Applying forecasting techniques to sales and business decisions can help companies predict consumer behaviour. Use relevant data that focusses on the factors impacting the retail business industry, such as consumer sentiment, available credit, employment factors, and wages. Relevant indicators of future sales can help project the likely consumer behaviour. For example, drop in hourly earnings is an indicator of a likely drop in retail sales.

Forecasting must also be applied to real business decisions. Be it marketing, staffing, or manufacturing decisions, accurate forecasting can help companies prepare for market opportunities by identifying potential growth markets and product lines.

3. Use of Predictive Analytics for consumer prediction

Predicting consumer behavior patterns based on customer interactions and transactions is extremely important in the digital era. Customer Experience analytics, Consumption based, spend analytics, Channel analytics or through digital footprints created by the user’s web browsing can be vital predictor data for driving insightful engagements in retail.

4. Target non-buying customers

In addition to predicting buying behaviors of existing customers, companies must also pay importance on how to make their products valuable for non-buying customers. Providing a solution for customers whose needs were not addressed previously, can open a wide and untapped market for retailers.

5. Create product promoters

According to the consultancy firm, Bain & company, consumer satisfaction and market share are not the right predictors for consumer behaviour. Customer responses to simple feedback questions such as, “How likely are you to recommend this product to your friend?” on a scale rating of 1-10 is likely to produce more accurate results. Product promoters with a rating of 9 or more, can be used as reliable sources of buying patterns.

Retailers need to develop a deeper understanding of these promoters, and also on how to convert more customers into promoters.

Insights Driven Sales = Customer Satisfaction

As clearly outlined by Lafley, retail businesses must clearly focus on the customer, and study the behavioral analytics of their customers. Customer Intelligence (CI), which is the process of gathering and analyzing data about customers, is clearly the need of the day. Such customer insights can help a business make a rational decision of their marketing efforts and product offering.

Vivian Gomes

VP Marketing and Inside Sales, CSS Corp

About CSS Corp Blog

About

The CSS Corp blog brings you insights from the world of technology and disruptions that are shaping the digital age. Subscribe now to learn the art of delivering exquisite customer eXperience for today's digital economy

Categories

see all