How Machine Learning Enhances Omni-Channel Retail Experience
We spoke to Valli Bollavaram, Vice President - Enterprise Data & Business Intelligence Engineering, Target India to understand the different ways by which machine learning enhances the omni-channel shopping experience
Pls share a brief background about Target India and its journey so far.
Target India operates as an extended headquarters to Target, the second largest discount retailer in the
“We have also seen great value in our supply chain optimizations with the help of Machine Learning. We can forecast sales more accurately at a local store level. We can also stock inventory based on the forecast and ensure we have the right level of stock for our guest needs.”
U.S. We began operations in Bangalore in 2005 with a technology unit that supports the US. Today, every business area at Target has team members in India. We have more than 2,800 team members supporting business areas such as technology, marketing, human resources, finance, merchandising, supply chain, analytics and reporting. We are a fully-integrated part of the global team. In December 2013, Target also launched the Target Accelerator program, that has been designed to help early stage startups develop concepts that could improve Target’s business and the broader retail industry.
What are some of the challenges associated with omni-channel retail?
Ensuring that the omni- channel approach to retail is executed seamlessly is important for customer satisfaction. It is not always easy to achieve.The biggest challenge that comes with ensuring this is
the expectation that omni channel retail provide uniform services to the consumers both in brick and mortar and online, andat the same time, provide a unique and complementary experience in all channels.
How does machine learning play a role in enhancing the omni-channel shopping experience?
Machine learning, a form of artificial intelligence, plays a huge role in building algorithms that would look at all customer touch points, understand life events and produce a personalized campaign, which would have a significant impact on the customer purchase behavior. Machine learning can enable sifting through vast amounts of unstructured digital information and learning on a continuous basis through previous computations, thus providing a very improved level of data analysis. Machines can forecast effective strategies and apply them for future, and can also forecast the impact of decisions. As decisions are fed back to the model, the impact of decisions are understood and the effectiveness of decisions are improved.
It therefore helps usto understand consumer shopping patterns across different channels, predict their interests and buying patterns. It also helps in providing assortment and marketing insights to the company. Retailing after all, is no longer a business of buying products for the chain and selling it to customers at the maximum profit.
How has Target’s journey in the machine learning space been till now?
Target is focussed on delivering convenience, customer satisfaction and better engagement regardless of the touchpoint consumers use when shopping. We have been developing machine learning algorithms for about a year now and have been using it in various data and business Insights products to help our business make decisions at the right time. Machine Learning has played an instrumental role in helping us enrich the overall shopping experience of our customers by helping personalise the assortment, provide personalised offers and optimize delivery.
Please provide us with some examples where machine learning has been successfully deployed across business processes.
We have been using machine learning for a long time now in helping make personalized recommendations for our online consumers, loyalty related and marketing decisions. We are able to influence the behavior across channels leveraging customer interaction in one channel to personalize the experience in another channel.
We have also seen great value in our supply chain optimizations with the help of Machine Learning. We can forecast sales more accurately at a local store level. We can also stock inventory based on the forecast and ensure we have the right level of stock for our guest needs. We could see this model self- evolving and improving to ensure the guest experience remains great. We pride ourselves in planning our assortments for our guests. We do the search, design and development of products that will be loved by our guests. We have been able to create great owned brands in Apparels, Food, Health& Beauty that are personalized to the guest preferences and enhance their experience.