Why analyzing consumer behavior through alt data is crucial for the future of retail

As the digital economy mushrooms, traditional transactional data can no longer supply the answers that retailers seek – alt data, on the other hand, is providing nuanced insights into buyer sentiment and consumer demand.

In this article, we will be looking at:

• A quick glance at alt data in retail 
• Traditional consumer data vs. alternative consumer data sets 
• How alt data is serving businesses in the retail and consumer space
• How you can leverage alt data as part of your business strategy
• The future of retail

A quick glance at alt data in retail 

Companies built on a consumer retail-driven business model are becoming increasingly data-driven. eCommerce and a blend of online/offline shopping are becoming a dominant consumer phenomenon. To this effect, corporations are looking for better ways to make decisions based on real consumer preferences and real-time trends as they unfold. To fill this need many retailers in the consumer discretionary spending space from fashion, and home appliances to gadgets and luxury goods have started leveraging alternative data sets. These include data sets pertaining to:

• Point of Sale (POS) devices
• credit card transactions
• website traffic
• quality and quantity of ad impressions
• brand perception on social networks

This information is helping companies make decisions such as where to stock product lines. For example, product ‘A’ may have positive consumer sentiment with customers in New York and therefore stocked in warehouses on the east coast, whereas product ‘B’ is gaining social traction in California and therefore stocked on the west coast. These insights have very real implications both on the cost of transporting goods as well as the speed with which items can be delivered to end consumers, and is a great way to illustrate the true value that alt data is producing in the retail sector.

Traditional consumer data vs. alternative consumer data sets 

Now that we are starting to get a sense for what type of data sets can be collected to benefit players in the retail/consumer space, let’s compare the two.

Classic bricks & mortar data may include:

• Sales per square foot
• Sales per employee
• Comparable store growth
• Inventory turnover/levels

Most of these data sets are collected manually and for those that have been hooked up to computerized systems, this type of data is typically only analyzed at the end of a quarter or sales cycle. 

With alt data, however, such as satellite imagery and credit card data, companies can focus on and keep track of:

• Brand value
• Brand perception
• Customer experiences both at physical and digital points of sale
• Product and collection momentum 
• Impact of ad and social impressions on consumer awareness and purchases

And then optimize sales processes, marketing efforts, production/supply lines to align with current alt data insights. For example, a company can analyze if consumer sentiment on social media is aligned with their brand messaging. Is consumer-driven buzz, content, and reviews discussing the product line’s major value propositions or missing the target entirely? In the case of not being aligned with consumer sentiment, a company can decide to react by strengthening its current value prop or pivoting to a different approach based on perceived demand.

How alt data is serving businesses in the retail and consumer space

These newfound alt data sets are driving retail businesses to increased growth and sales and are enabling them to be more agile. One example of this is companies with a focus on eCommerce who are successfully implementing quicker than ever product cycles. If once upon a time a company would roll out a product line for a season and only draw conclusions from Q1 in Q2, and apply insights in Q3 and 4. These same companies are now able to create new products in the same quarter as consumer sentiment data becomes available, rolling them out (from production to shelf) in a matter of weeks. 

Other ways in which successful companies are integrating alt data into their day-to-day operations to grow revenue margins include tracking key performance indicators, such as:

• SKU level availability
• Daily pricing fluctuations
• First-party webstore purchase levels 
• Third-party marketplace product turnover 
• Product innovation tracking
• Availability issues
• Markdown and special promotions

And implementing these into the decision-making process. 

How you can leverage alt data as part of your business strategy

Here are 4 things you can do with derived alt data insights:  

One: Understanding consumer sentiment

As we mentioned earlier, consumer sentiment is paramount which is why retailers are closely following alternative data sets regarding:

• social media buzz around certain products
• shopping patterns on eCommerce marketplaces 

Companies use this data to anticipate consumer shopping trends before competitors do, enabling them to win over hefty chunks of market share. For example, a supermarket (with both an online and offline presence) that identifies a rise in interest and actual  purchases of products that imitate meat, may choose to stock up on these foods. Target data sets may be collected directly from ‘Amazon’s grocery’, ‘eBay’, ‘Facebook’, and other marketplaces/social networks. In turn empowering groceries to make consumer-driven changes and win over new customers who are looking for healthier, ‘planet-positive’ food choices.  

Two: Being omniscient or at least omnichannel 

Companies who have a strong physical presence and start collecting alt data may discover that a large part of their target audience is actually making purchases online. This may lead them to take the plunge toward omnichannel retail which entails selling goods through a wide variety of platforms and channels:

• Physical locations
• Web stores (typically brand based such as Nike.com, homedepot.com etc)
• Third-party marketplaces (Amazon, eBay, Wish)
• Applications
• Social media (such as Instagram and Pinterest that have new app-native sales models)

Three: Implementing insights in real-time

A good example of how real-time insights can be effective is a direct competitor who is trying to undercut your pricing, and special offers. Modern price and value-driven consumers are very savvy and for the most part check a product’s prices and value proposition on multiple marketplaces and websites before making a final purchase decision. 

In this instance, you can leverage alternative data such as consumer search engine queries, for example, ‘best flight and hotel deals to Vegas’. When you know what type of ‘packages’ or ‘vacation deals’ consumers are looking for, you can combine this information with classic data such as competitor pricing. At this point you will really be able to compete both on price and value resulting in higher conversion rates.

Four: Syncing stock with consumer demand

Stock syncing is not a new practice. But the ability to control stock levels as a function of competitor levels is definitely a novelty.  A nice example of this is using alt data to identify competitors who are receiving traffic and orders for a specific item but are out of stock. If these missteps are properly identified, one could stock up on such items and attempt to divert purchases to their outlets, and potentially gain returning customers over the mid to long term. 

The future of retail 

As the economy continues expanding online at exceptionally higher rates than previously expected, digital commerce is and will continue to follow suit. Classic data sets will no longer cut it and companies who do not adapt their retail models accordingly will fall behind, and lose their relevance. 

It will become existentially important for businesses to make all of their decisions based on 

• consumer demand and sentiment 
• competitor offers, production, and sales volume 

 as well as other ‘on-the-ground’, ‘in the moment’ alternative data sets.