Part 2 in the “Embedded Analytics” series that explores the many ways that companies across industries are using analytics to support innovation.
In the experience economy, companies understand that customers value positive experiences above all else. If you don’t make the interaction easy and enjoyable, customers will go elsewhere fast.
Indeed, customers have already moved in droves to the companies that transform data into insight and predictions in order to create the magical experience that customers crave. Why wait until a customer searches for a product when you can analyze what will be needed next and offer it to them proactively? Why try to win back dissatisfied customers after they’ve left when you can predict the risk of churn and address their issues before they leave?
So, what are some of the key focus areas when it comes to using data to improve the customer experience? Let’s have a look.
Even though marketing, sales, service, and operations may all function separately, customers interact with your brand, not your organization. They should always encounter a single seamless interface no matter where they may be in their journey.
A customer shopping online should be understood as the same customer across all channels – phone, social media, in-store, wherever. This requires a 360-degree view of the customer built by merging all the customer’s identities, preferences, and touchpoints with your brand. If this view is used across all the organizations in your company, you can deliver a truly connected experience.
Let’s say an online customer returns a purchase at a store location – or calls on the phone to complain about a late delivery. No matter where your employees are, they should be able to respond effectively based on a single 360-degree view of the customer that includes purchase history and complete profile.
The richness of the profile and customer insight helps to define a coherent experience and can differentiate your brand in the market. Embedded analytics can help deliver that insight and orchestrate the customer journey.
Consider, for example, e-commerce personalization. Based on a single source of truth, your company can continuously update the customer profile based on past purchases, social media tracking, sentiment analysis, and shopping habits. Using insight into the customer’s interaction and behavior, your e-commerce site can show the customer just the right offering, increasing the likelihood of purchase and optimizing the shopping experience.
At this level, personalization requires sophisticated analytics such as machine learning algorithms that identify patterns and make predictive recommendations. Complex machine learning scenarios maintained by teams of data scientists are unrealistic. Increasingly, organizations are looking for algorithms that are embedded in processes such as lead conversion probabilities, churn risk scores, product recommendation models, and sales forecasts. These models consume all the data generated across various processes and convert them into timely actionable steps. User-friendly machine learning like this is helping organizations to capitalize on their data to deliver connected journeys and better experiences.
Connected supply chains
The sales experience, of course, is only one aspect of the connected customer journey. When a purchase is made, the rest of your operations needs to kick into gear.
Sharing customer insights from beginning to end plays a key role in making it all happen. Fulfillment and final delivery processes need to go off without a hitch – which calls for advanced logistics with total transparency to enable customers to track their goods across every delivery milestone. Special customer requests, for example, might require unique configurations; hence, tie-ins to manufacturing and assembly units are essential.
The common thread that runs from designing the product to delivering it (or operating it, as is the case with many B2B scenarios) is a connected supply chain. From sensing demand and responding with new offerings to delivering the goods and monitoring customer feedback, the goal is to use data to orchestrate all the activities of the end-to-end supply chain .
Increasingly, with data coming in from sensors on connected assets, organizations can glean important insights regarding asset status and health, even predicting the breakdown of parts before they cause disruptions. Now you can optimize maintenance, delivering it when it’s needed instead of “just in case.”
You can also innovate your entire business model by offering “assets as a service.” With this model, the customer pays for outcomes (water pumped, factories cooled, pages printed) instead of a one-time purchase. And these payments continue indefinitely. The success of the service-based model also depends upon understanding the relevant usage and pricing metrics embedded into the process to optimize the customer experience.
The key to better customer relationships is listening. With data analytics, there are many ways to listen.
Sentiment analysis from social media can provide indications about a product launch or how customers regard ongoing services. Help-ticket analysis can pinpoint persistent customer complaints. Demand sensing can help inform business planning and turn usage data into ideas for new offerings that customers want – maybe before they even know it.
These are just a few examples. The point is that data across all channels and customer interaction points needs to be consolidated and available for advanced analysis. In the experience economy, this is what listening entails.
Take, for example, the online shoe store that knows the size you need based on data from previous purchases – including shoes you’ve returned and those you’ve kept. With analytics that understand how sizing differences fluctuate between, say, Nike and Adidas, the retailer can recommend the right size for, say, TOMS or Under Armour. By “listening” to past customer experiences in this way, you can improve the experience today. The customer receives the right product the first time, while the merchant avoids return shipping and related logistics costs.
Increasingly, we’re all being evaluated by our customers on the experiences we deliver – and with sophisticated analytics, many companies are finding ways to deliver. But sophistication doesn’t necessarily mean complexity. Today, powerful yet intuitive analytics are being embedded into processes and delivered directly to the business user for real-time insight. The road ahead is open for those who take advantage of it.
For more information, download the March 2019 Forrester Consulting opportunity snapshot, “Optimize Business Intelligence Efforts With Embedded, Application-Driven Analytics,” to examine the challenges, best practices, and recommended actions that can help your sales and marketing organizations turn your business into an intelligent, competitive force.