This is a guest post from David Stephenson, Chief Data Officer at DSI Analytics.
In days long-past, when a store manager understood and interacted individually with each customer, the manager had a fantastic capability to understand and to help each customer. In our age of ecommerce, with millions of digital customers, recent technology developments have brought us back to the point where we can recreate that personal experience. This is critical to understand, as shopkeepers who only interact with customers while ringing up purchases are as destined for failure now as they were in the old days.
Online Customer Journey
If I’m running an online store, I can approach my digital in four ways:
It’s the Big Data method that allows me to truly understand and eventually influence customer journey.
The Realm of the possible
These days there are also relatively painless ways to start using Big Data to recapturing personalized customer interaction (See my recent article Useful Big Data: The Fastest Way for more practical details).
With relatively little effort, we can
How does this look? Let’s walk through the process that a traditional shopkeeper would follow, and realize that we can again do this with our millions of digital customers.
Customer Intent
Right from the start, the successful shopkeeper finds out all he can about the person visiting his store, looking to understand things such as
By getting answers to these questions for digital customers, we can optimize our marketing efforts, our landing pages, and our product mix.
Customer Preferences and Priorities
As the customer continues the shopping process, the shopkeeper will begin to understand the preferences and priorities of the customer.
The customer may express these preferences outright, or the observant shopkeeper may imply them by watching the customer’s actions…which products are ignored, quickly put back, or re-considered multiple times. Getting that level of behavioral insight for millions of digital customers is amazing, and it is possible.
Proactive Shopkeeping
The shopkeeper will help the customer. He will recommend products most likely to sell (or perhaps with the highest expected profit margin!)
The shopkeeper will keep two things in mind when making these recommendations:
The shopkeeper will be very clever in how he nudges the customer into making a transaction. For example, he’ll know when to offer spot sales. He’ll learn when to make certain inventory prominent and which features of each product to highlight for each customer.
It’s not enough to say ‘most customers bought this product’. It needs to be ‘most customers just like you bought this product’. It can’t be ‘sales increase when I run a 10% discount’. It needs to be ‘sales increase on this product to this customer base when I offer a 10% discount at a particular point in the shopping journey’.
Observant
How does the shopkeeper know how to personally interact with each customer in a way that produces the most sales… this process that we call ‘conversion rate optimization’? It’s by remembering what has worked with customers in the past. The shopkeeper observes and remembers the entire shopping experiences of all past customers. He can then make observations such as
Most people who came looking for phone X and then moved on to consider phone Y and Z ended up purchasing phone Y; if they also sorted based on price, then conversion rate increased by 7% when a 5% discount was offered on phone Y.
Wow. Now I know, for a particular segment of customers, how to both make more money and to have a happier customer.
But not intrusive
Two important things to watch here:
And always learning from the customer
Eventually the customer makes the purchase, or perhaps doesn’t make the purchase, but either way the shopkeeper knows much, much more than he would if he’d just focused on the point of sale. Not only has he increased customer satisfaction and increased the chance of sales, he has developed his understanding of what works or doesn’t work. He’s learned what is important to customers and can direct his purchases, marketing and product placement accordingly.
Perhaps the customer was very well prepared, and the shopkeeper has even learned something new about his own products, such as cross-sell or substitutionary potential. He can later use this new information to make better recommendations to future customers.
To sum it up
Using Big Data technologies, today’s shopkeeper can again understand the preferences, intentions and habits of customers at extremely high level of detail, allowing him to understand and help them in much more insightful ways, thus both increasing his various conversion rates and also providing the customer with a much better shopping experience.
All in all, it’s a lot of potential insight, and it can make a massive difference in revenue.
This is a guest post from David Stephenson, Chief Data Officer at DSI Analytics.
Bio: David Stephenson works in the fields of Data Science and Big Data Analytics. He has led global analytics programs for companies such as eBay, Axel Springer and Coolblue (a skyrocketing ecommerce player in the Netherlands). David has also worked extensively in insurance, capital markets, and financial risk management and has served as an expert advisor to top-tier investment, private equity and management consulting firms.
David completed his Ph.D. at Cornell University and was subsequently Professor at the University of Pennsylvania, teaching applied analytics to graduate students in the engineering and Wharton business schools.
David is currently based in Amsterdam.
Check out DSI Analytics and connect with David on Twitter @Stephenson_Data and at LinkedIn here.
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