Technology Is The New Personal Shopper: How To Use Data To Transform Your Customer’s Buying Experience

If your company is using predictive analytics to spot market trends and anticipate seasonal needs in your stores, congratulations! You’re well on your way to using data to transform your customer’s shopping experience.

Today, predictive models and data based insights are not just being used to predict overall market trends, but to create a highly personalized shopping experience for each and every customer.

By using technology to make every customer feel they have their very own “personal shopper,” retailers are finding powerful ways to stay ahead of trends and blow the competition out of the water.

Here’s how the personal shopping experience plays out using strategically chosen customer data.

Close Relationships Creates Increased Loyalty

In the digital age, the most powerful and practical way to increase customer loyalty is by truly getting to know your customers.

While you can’t take every single customer out to lunch, you can create special marketing offers based on highly personalized information and life events.

For instance, by tracking a customer’s purchase of items like a car seat or stroller, you can create special discount offers on other baby and pregnancy-related items.

Not only is your customer much more likely to respond to a product offer that’s based on data; they’re also much more likely to remain loyal to a company that understands who they are and where they are in their lives.

Specific Data is Applied to Different Demographics

How can you possibly get that personal – we’re talking personal-shopper personal – with each and every customer?

It is simple. It is accomplished by applying transactional and behavioral data to all of your customers that meet certain criteria.

In the above example, you could create a predictive model that assigns certain discounts and product offers to all customers who purchase car seats and strollers, as long as they also meet specific demographic criteria such as gender and age.

Predictive models let you create a highly personalized shopping experience without spending time or resources ‘interviewing’ each individual customer.

Instead, all the specifics you need can be found in the data and applied across a wide range of relevant customer segments.

Different Data Types Lead to Deeper Insights

So far we’ve mainly looked at purchase history to build our predictive models. But there are many other types of data that can be used and combined to create a more personalized shopping experience.

These include:

• Attitudinal data – this type of data is interested in finding out why customers make certain purchases and how they feel about those purchases. Attitudinal data is best collected through personal surveys and market research conducted on social sites, online forums, and industry-specific blogs.
• Descriptive data – there may be a variety of ways of describing your customers, but how do your customers describe themselves? Combine these self-descriptions with demographic data like zip code and street address to create a three-dimensional understanding of your customer.
• Interactive data – how does each customer interact with your brand? Finding out is as easy as extracting data from your customer service center or eCommerce store. Look for questions, ratings, reviews, and complaints to understand your customer’s perceptions of your brand.

Basket analysis leads to future purchase predictions

Reviewing your customers’ most recent purchase history will not only help you predict when they’ll buy next; it will help you predict what they’ll buy next.

For instance, you can use predictive analytics to see which products are most typically bought together, or which products tend to follow one another in the purchase cycle.

We frequently create the following types of predictive models for our clients:

• Customers who make more than one purchase in category X are highly likely to purchase from category Y as well
• A customer who purchases product A is highly likely to purchase product B the next time they shop
• If customers purchase product C at the same time as product D, their next purchase is highly likely to be product E

These predictions are then used to determine which marketing images and offers are used for particular customers. A customer who purchased product A receives marketing materials featuring product B, and so on.

Thanks to the power of data-based technologies, customers have come to expect a more personalized shopping experience. By using predictive analytics and making predictive models, you can create personalized interactions and branded tracking pages that can be applied to a wide variety of customer demographics, all while generating a good ecommerce customer experience for every individual.

Food for Thought: Chocolate Almond Torte

Once you create a personal shopping experience for each and every customer, it’s impossible to go back to marketing the old-fashioned way (you know – sending every single customer the exact same marketing message!).

I feel the exact same way about chocolate.

After my trip to Italy last year, I realized that chocolate is truly a requirement of every dessert. There was no going back and no compromising!

My latest Italian obsession is a Chocolate Almond Torte that calls for a 10″ diameter springform pan, but I use a 9″ pan because I like this torte just a bit thicker.

Because I am a fan of most things PBS, I watch America’s Test Kitchens regularly and have one of their massive cookbooks (thank you WCTE-TV, Cookeville, TN). Mr. Kimball and his Test Kitchen colleagues discovered that Ghirardelli is the most preferred brand of baking chocolate according to their chocolate-cooking taste test.

For this recipe I use two Ghirardelli bittersweet bars, which are easily found at most grocery stores. The combination of an Italian almond torte recipe using Ghirardelli chocolate will truly make your brains fall out!

To get the recipe, click here: Chocolate Almond Torte

Next blog, in the third installment of our series on redefining the customer shopping experience, I’ll reveal how you can use mobile marketing to predict and direct customer purchases while increasing profits. (For part 1 of this series, click here).

Connect with Massa & Company for a free consultation on using data to increase profits and reduce your marketing spend. Visit https://massainc.com/ or call (312) 463–1050 to set up your free consultation today.