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Machines Are Key to Creating, Growing and Maintaining eCommerce Relationships by Sri Kothur
October 9, 2018
While ecommerce only accounts for about 10 percent of total retail sales, 88 percent of the search part of the shopping process is done online, according to the Ecommerce Foundation. That’s why maximizing your digital connections is so important. But in a rapidly growing, increasingly crowded market, it can be hard to break through the clutter.
Here’s how machine learning can help you expand markets and deepen relationships with customers.
Shoppers’ Rising Expectations
Shoppers now expect a voice in what, when and how they shop and buy. How you provide that voice can make or break success. For example, ecommerce allows you to follow up with consumers who browse online but don’t buy. However, if you fail to strike the right balance between motivating sales and pestering consumers, your “browsers” may never return.
Merchants targeting multiple generations also must consider how to deliver customer service to consumers with vastly different expectations. Younger generations have been raised in the self-service era, while older generations have formed their shopping habits in the days of personal, in-store service. While chat-bots may satisfy the needs of younger consumers, older shoppers may expect human chat.
Finally, your search engine ranking now may be as important to success as the location of your store.
How Machine Learning Helps
Machine learning can replicate many of the activities knowledgeable salespeople once carried out. They help familiarize you with customer habits, find ways to expand your business, and can help you pivot your business if necessary. They also perform diagnostics on customer journeys to help you:
- Determine where your customers come from – their location, the browser they’re using and more
- Optimize search results by pulling information from deep within search patterns
- Examine how consumers navigate through sites and flag obstacles they face along the way
- Act as virtual salespeople to prompt sales, cross-sell and upsell. (Amazon’s Recommendation Engine accounts for 35 percent of its sales.)
- Direct consumers to additional information sources, including live chat, which can help close the sale
- Optimize and individualize pricing according to dynamics such as changes in competitive pricing, market demand, and consumer behavior
- Reduce chargebacks by preventing fraudulent transactions
- Perform customer service duties through chatbots
- Predict supply and demand to help make informed product decisions
Netflix is a good example of a company using machine learning to improve the quality of its services. Machine learning helps it analyze where product improvements need to be made, how to target resources most effectively, respond to network issues in real-time, determine tradeoffs when balancing customer service versus company expenditures, anticipate what users want next so it can deliver content faster and detect user device changes so company engineers can respond to different needs.
The most successful users of machine learning are those who are committed to continuous improvement to keep pace with rapid market change and maintain market differentiation.
Machines can’t solve every problem. They’re also no substitute for human insight that skillful merchants bring to the table. However, machine learning has become a valuable tool for unbiased decision-making, improved customer experiences, and reduced fraud.