Over time, the increase in the number of avenues through which consumers interact with your brand has contributed to the exponential increase in the amount of data at the retailer’s disposal. And it is showing no signs of abating. Although it adds several layers of complexity in how you deal with this constant stream of data, tapping into it to uncover ‘in-the-moment’ opportunities will make it all worthwhile.
However, that’s easier said than done. Clearly, the real challenge lies in how you can harness the right online and offline data efficiently to better understand and engage customers at precisely the right time. This capability alone will separate retailers that thrive from the rest in the years ahead.
That begs the question, which data is useful to deliver hyper-personalized experiences that consumers expect from retailers today? How can it be harnessed? Here are some examples:
1. Online Data is Just Half the Story
Granted online data is easier to gather and use – to deliver personalized experiences. But simply using in-store purchase data can significantly enhance the shopper experience. After all the lines have blurred, and consumers shift effortlessly between the website, mobile app, or the physical store.
Click and mortar stores can gain a significant advantage over pure-play commerce stores if they can better utilize this data for an omnichannel personalized experience. The key however is arriving at the right mix of offline and online data with a reasonable level of consistency. AI-driven omnichannel personalization solutions can help. Believe it or not, a simple tag like “products trending in-store” can reduce friction through the online shopping journey.
Some of our customers have reported an increase of over 2% in their average order values just by bringing offline data to refine the online experience. To add to that, more unified experiences are offered to customers by way of recommendations across product categories. For instance, the AI can automatically filter out poorly rated footwear products and eliminate them from recommending those to consumers in their email campaign with offers for matching clothes. Besides, the system does all this automatically, eliminating the need for manual merchandising or interventions.
2. Using Back Office Data to Increase Margins
Sure, you want to deliver individualized experiences, but at the same time, an area that is worth focusing on for retailers when it comes to increasing profits and margins is back-office data. Some avenues to explore:
- Margin data: Using this data, customers can be guided to higher margin products through boosted search results, promotional offers, and recommendation blocks. These high-margin products when intelligently combined with loss leaders have shown they can boost profitability. These can easily be tested by the system.
- Brand: Brand affinity is a great way to boost margins. Similar brands at similar price points can be linked up and budget brands can be suppressed within search results to drive more return visits, perceived relevance, and larger basket sizes. The critical part is that the AI delivers the best results for affinity-based products by determining the right price point ranges for related products.
- Regional store/inventory: Using regional inventory data can help in adding a layer of personalization to the online buying experience, help drive demand and reduce out-of-stock disappointment, while driving up profitable store visits.
3. Fixing the Unstructured Data Problem to Solve Unsolved Problems
One of the common challenges with data has to do with poor product feed data, whether it is to do with quality or depth of it. Many approaches can be used to expand data to increase sales.
- Starting simple helps. For example, you might have a range of kitchen accessories, let’s say for a brand called “Denver”. Using simple rules, you can boost the product name “Denver” by promoting other items from that brand. The rule would have an if-then clause like ‘if the product name contains the term “Denver”, then boost other products containing the term “Denver” from the same category.’
- Deep Learning AI lets you understand how customers research your products before making a purchase decision. Descriptions, comments, social posts, tags, and other such data are crucial in helping customers find their choice of product, while also helping them realize their ‘intent’ of what they want – all through the power of AI.
When you link ‘similar’ products together based on common concepts (e.g., easy, simple, long-lasting, rugged, etc.) and start to understand the ‘why’ behind a purchase decision (e.g., long-lasting frying pan for someone who camps out a lot), you get access to a whole host of products that you might have never guessed would be a perfect fit for some of your customers.
Bottom line
It’s true that data can be invaluable, but you do not need to use everything that is available. Begin by using small sets of relevant data as mentioned above. When coupled with AI, it can give your business a real shot in the arm.
