BRAND STORY When the pandemic temporarily shut down stores, the retail chain set out to feed its predictive model by amalgamating a wide variety of data streams
Kohl’s has understood that customer behavior is unpredictable, thanks to the transient personalized shopping experiences. Proprietary data systems that thrive on information sourced at transactions and streamed to operations appeared to be fading for this historical retailer. To step out of the shadows, it has deployed a “sense and respond” policy to meet the rapidly changing customer behavior.
Kohl’s sensed its customer by harnessing a total view. It tried finding answers to questions such as: what was the customer interested in? What economic pressure or difference were affecting their behaviors? To access these external data streams and know their customer better, Kohl’s reached out to Deloitte Digital.
They have been amalgamating a wide variety of data streams – from web clicks of millions of consumers to the cross-spending trends – they have it all assimilated in a centralized pool. Artificial intelligence employed by the retailer fished out the key insights from the vast pool to develop predictive models.
This will not only help Kohl’s to launch new products tailored for new shopping patterns but also optimize product placement at stores in response to trends of those locations.
Kohl’s has traveled the length of its customers ever since it opened its first store in 1962. Before becoming a giant retailer, it was a nonchalant grocery store in Milwaukee started by Maxwell Kohl – a Polish immigrant – in 1927. One could only imagine a wide-eyed young man trying to seize the American Dream, which is considered the gold standard of opportunity and success. Maxwell dared to spend all his savings on the store.
Three decades down the line, Kohl’s happened. Maxwell Kohl used his experience with supermarkets to develop customer service and manage inventories—an uncommon sight at the department stores back then.
His shrewd entrepreneurial skills allowed the stores to offer lower prices than high-end stores and higher quality than the discounters. The formula worked, and Kohl’s became a successful chain, which the family ran till 1972. It was then sold to Brown and Williamson.
In March 2022, Kohl’s transformed into an active and casual lifestyle destination. The retail chain’s off-the-mall footprint will perhaps help it sustain this post-Covid19 transition. However, some critics say that Kohl’s’ success at the time of inflation will determine how much market share it acquires, especially when the middle of the retail market is collapsing.
At such a time, Kohl’s is focussing on improving store performance by marking its customers with managers so that they are guided at every step. It has also boosted its digital presence by harnessing data from its in-store footprint. Analysts predict that Kohl’s could well maintain its $20 billion revenue despite Covid19 shocks.
Meeting customers where they are
Covid-19 forced many retailers to pull down their shutters. The pandemic changed traditional data patterns—a consumer’s immediate needs took a backseat. None of the predictive mechanisms worked, pushing many retailers, including Kohl’s, into uncharted territory.
In such circumstances, Kohl’s depended on its need and ability to respond to its customers. The retailers merged internal and external data streams to meet their customers where they are. And Kohl’s did it quite literally through launching facilities like curbside pickup – an early success.
This accessible feature was put into action promptly as Kohl’s had been planning it long before Covid-19 struck the world. The groundwork was ready. The temporary closure of stores only accelerated the emergency. Through efficient monitoring and combining data, Kohl’s knew where curbside services were needed the most, and they were implemented likewise.
Equipping managers with real-time customers
Now with the pandemic waning and stores reopening, the traditional customer trends have re-emerged. Kohl’s has armed its staff with data for a seamless experience on the ground.
A store manager at Kohl’s once told its former President Sona Chawla that a particular item was not selling well in her store but was doing well in other stores. With a visual merchandising recommendation, she figured out that the way the product was stacked in a display made it difficult to see and access. Rearranging the display helped drive conversions of the product. The manager ticked off her task for the day.
The tasks for the managers are part of Kohl’s’ actionable analytics initiative. The program is an appendage to Kohl’s dashboard, which shows its stores sales data and other daily statistics.
The actionable analytics arm managers with tasks that Kohl’s determines by using customer data, comparable store data, and machine learning algorithms. The suggestions to the managers are varied. They get to know what customers are looking for in a particular section of the store or buying online. It also shows which products are being sold in departments of different stores.
The artificial intelligence component can anticipate how certain inventory might sell based on online searches and other store performances so that the store managers can stock the floor properly to prevent it from getting cleared out within minutes by consumers. Moreover, the real-time data also allows store managers to get staffing recommendations from the dashboard, around how many people to keep on the sales floor or the stockroom during peak store hours. It also helps manage additional staffing needs and helps them check on how products are selling.
Cradling the app
It would not be wrong to say that in the past decade, the mobile application has gained an edge over store-shopping – especially fuelled by Covid-19. Stores have become mere extensions of e-commerce fulfillment networks.
Since its hike towards digital innovation, Kohl’s unflinching priority has been providing experiences on its app, engaging its customers on personal levels, delivering exciting products, and catering to its patrons across multiple channels.
This philosophy has given birth to facilities such as “Buy online, pick up in store” and “Buy online, ship from store”. With help from IBM, it also launched a savings wallet for shoppers. In the app, users are rewarded for transactions and behaviors, and they also have the option to use their points for purchases or donate to charity.
Kohl’s has been aware of its volume of customers. Consequently, it has endeavored to cut its blanket policy into arrangements that fit diverse and emerging needs. They have been able to meet a customer’s needs in real-time as they fed data into operations, creating an easy, tailored process. It is a win for all those involved – the patron, the employee, and Kohl’s.
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