Time hasn’t chipped away at the retail essentials. Rather, the tenet-trio of customer engagement, customer experience and customer shopping have evolved with time and have also grown in significance. The days when customers just relied on the corner store to interact with the retailer, purchase products to satiate their needs are long gone.
Flipping out his mobile, a customer looks for information pertaining to a product. Wanting to know more about the salient features associated with the product, the customer makes a deep online dive, scours the internet to find all the relevant information he wants. Armed with details, the customer now visits the nearby retail store to buy the product of his choice. As he walks down the beacon-powered aisle, the customer’s mobile toots, an inkling into the personalized offer that the customer has received from the retailer. And the customer is floored with the ‘price offer’, which happens to be the best he has come across during his extensive research about that product.
Both physical and online channels excite the customer today, empower the customer to take a decision as to how, when and where he needs to buy a product, interact with the retailer. Proliferation of customer channels also means that the retailer has abundant data to deal with. And it is this data flowing through the omnichannel retail that can give shape to the customer-envisioned retail – when advanced analytics, machine learning and big data come together to help the retailer read customer intentions, preferences, needs, behavior, and action well in advance.
How then omnichannel analytics transforms retailers into ‘customer-envisioned’ retail outlets?
Leveraging channel data to know who the customer is
In the omnichannel scenario, there is customer hopping from one channel to another – encompassing both physical and digital world. Data from various channels gives retailers the lead to unearth the ‘customer persona’, from knowing customer likes, dislikes and behavior to preferences. The promise of data is realized by feeding the physical and digital data into ML algorithms, which can yield patterns for the retailer to embrace a proactive approach to cater to the customer.
Using touchpoint data to make predictions and prescriptions across operations
The range of touchpoints at the disposal of customers also leads to copious flow of data across touchpoints. While the customer interactions and communications combine with the context and data, retailers get closer to predicting emotional state of customers and prescribing actions that trigger desired and successful outcomes.
Tapping into customer journey data to read ‘intent’
Unlike the olden days, customer journeys today can include many channels, allowing retailers to make the most of this customer journey data. Leveraging intent-based segmentation model and customer journey data, retailers are better equipped to unearth the reason behind a customer’s visit. In this omnichannel scenario, the model accommodates various touchpoints including mobile, social media as well as the store.
Rolling out personalized marketing programs
Omnichannel data also helps retailers go beyond mere customer segmentation to build micro-segments. There is also a difference between a price conscious customer and a brand conscious customer. With micro-segments, facilitated by using data across various channels, retailers can predict customer behavior and pinpoint preferences to recommend the right product to the right customer.
Enabling seamless customer experience
The transition from omnichannel data to omnichannel success hinges on the retailers’ ability to use advanced analytics techniques on the touchpoint data, gain prescriptive power to take the ‘Next best action’ and assure connected experience. With advanced analytics techniques acting on data, retailers understand customer affinity towards channels, build intelligent campaigns and make intelligent decisions on the go.
At Saksoft, we take cue from the data footprints left by the customer across channels, leverage ML models, and make the most of channel, transaction and customer data to extract value from omnichannel analytics and help retail outlets make the transition to ‘customer-envisioned’ retailers.
