Few sectors experience change as relentlessly as the retail industry, and with customer demands in constant flux, keeping up with market shifts can be a challenge. This has given rise to an increasing amount of retailers turning to analytics-based insights for better customer engagement and brand positioning. And, with new customer touch points springing up almost everywhere imaginable (think social media, online retail, mobile app data, etc.), retailers are in no shortage of potential customer insights. Social, transactional, professional and other data can be fairly straightforward to obtain in our digital milieu, provided you understand which engagement levers to pull. So how does data analytics help retailers reach the ever-fleeting modern customer? Let’s take a look.
In This Post
Helping customers find their way to your brand
While product recommendation has been around before the days of the personal computer, analytics platforms are taking it a few steps further by painting a detailed picture of the customer as a persona to better understand the buying – or disengagement – triggers in the minds of Joe Consumer. For example, location-based mobile services, known as SoLoMo, (Social, Mobile and Location) are informing customers on products and offers pertinent to their buying habits as they visit malls or other retail areas.
Through analytics-based content delivery, that incorporates wide sets of customer data, including the stores they visit, products they buy, personal reviews and so on, location-based apps are helping brands connect to customers in a much more personal and immediate way. When converged, SoLoMo data helps retailers understand customers’ movements and buying patterns on a far more granular level; including how they respond to special offers, repeat visits, interest in products, etc.
Its time to get personal
Today, making that connection is more important than ever, with more than 70% of customers stating that they prefer doing business with a brand that provides them with informative recommendations on products that they actually take an interest in. Simply put, if a customer doesn’t get the quality of service and product s/he wants, they’re out of your brick and mortar, or online store, in the drop of a hat. In what Forrester Research calls the age of the customer, individual choice and personalization of goods is fast becoming a key differentiator. To cater to this new trend in behavior, companies need to understand the driving factors behind what makes a certain product or offer appealing to the customer on a personal level. Amazon still stands as the prime example of personalized product recommendations – and it’s no wonder. The online retail giant understands the value of connecting with customers through speaking “their language”. By drawing on shopping cart data and a host of other information sources, the retailer manages to engage customers with content and product offers that speak to them, personally.
Making inventory management more predictable
Overstocked or understocked; neither is a situation any retailer would want to find themselves in, since both situations imply revenue-loss. Predictive models are helping retailers, from restaurants to sneaker stores, plug inventory gaps by providing up-to-the-minute insights on the movement of stock across the entire supply chain. Analysis of historic data also allows for better forecasting of customer demands, thus placing retailers on the frontfoot during high influxes of customer traffic.
For example, historic data on Christmas season sales can help retailers better prepare for increase in demand for certain products, while simultaneously helping retailers fine-tune product launches and placements strategies. Correlations between between stores and products in certain geographical areas can give insights to better inventory planning and distribution according to demographic and personal preferences relating to socio-economic and other data sets.
Tough markets and picky customers are voicing the call for more data-driven insights
As customers demand more upfront value demonstration, and the competition wises-up to the value of data-driven insights, retailers that are seizing the opportunities resident in their information assets are poised to gain the upperhand. While it’s no secret that people still prefer the personal touch when it comes to brand engagement, the challenge will be in finding the opportunities, through the vehicle of analytics, to hit the right notes in the hearts and minds of their customers.
About the Author
Ajay Khanna is CEO and Founder of Tellius with vision to re-define data intelligence by combining power of search with predictive analytics. Ajay has background with building and growing successful innovative startups. He is a passionate Tech innovator with experience in building new technologies and disruptive business models. Ajay was previously CTO of Celcite, the leading company to build and launch SON (Self optimizing networks) solution and played key role in scaling the company with successful exit. At Tellius, we love data and we believe it can transform your business – ask us how. Contact us to learn more about how we can help you make your data work for you.