Whether they are in-store and ready to buy or just doing some online research, consumers these days demand a tailored “just-for-me” experience. Customers can buy from almost anyone anywhere. They are mobile, connected, and for the most part, well-informed. These expectations extend beyond the consumer; they are equally true for business-to-business interactions.
High-tech companies have the ability to fundamentally change their engagement with customers across many channels using Big Data and the Internet of Things. Competition is about products as well as gaining the customer’s mindshare. Customer service is now more important to a brand than ever. Customer expectations continue to rise and learning to recognize and understand these behaviors is paramount to success.
But first, what is customer engagement? As a brand, the customer experiences you create should prompt customers to interact with, and better yet, share your story. Strong customer engagement strategies foster brand loyalty and in return, greater profits. As companies continue to collect data at a fast rate, data mining can build a complete digital picture of the customer. This is where predictive analytics come in. Predictive analytics can let you know when and what a consumer will purchase and maybe most importantly, how much they plan to spend.
Gathering the data is just the beginning. Mining the date points and know how to implement the results is an entirely new issue. In simple terms, predictive analytics means using quantitative methods to check data and taking those insights and using them to shape future business decisions.
Increase cross-sell and up-sell
Electronics superstore Best Buy discovered through data analysis that 7 percent of its customer base was responsible for 43 percent of its sales, according to a report from Accenture. Using this data, the retailer was able to segment its customers into different archetypes, basically deciding which customers spend money and which don’t. Using the data, it redesigned stores to create a new experience that reflects the particular buying habits of customers who actually spend money.
In this case, predictive data analytics and the Internet of Things allowed Best Buy to focus on the small number of customers. But those relatively few customers represented a large percentage of transaction. Instead of wasting money sending offers to everyone, Best Buy used its data to pinpoint customers who were more likely to buy.
Accenture’s research shows that companies that embrace predictive analytics in their decision-making reap big rewards.
Reduce customer churn
Many businesses live and die by their customer churn rate. Churn is an important analytic, especially if you have customers subscribing to a service such as LinkedIn’s premium membership or Adobe’s cloud-based subscription services.
In the past, Adobe sold boxed software, but now its business is driven by a cloud-based subscription service that pumps real-time updates to users. Instead of releasing new software updates every one to two years, Adobe can send updates to users at any time. This shift in business models has transformed the types of customer, product, and sales data to which Adobe has access. Data collection used to be limited to name, address, and phone number. But now, Adobe is collecting data on how customers use a particular product. This data and the use of predictive analytics helps Adobe and other businesses understand the needs of its customers now and in the future.
Predictive analytics creates models that know when a customer is at risk of churning. The business can take the information and act proactively to address the pain points or key interests of the customer. And not only will the data tell you that a customer is about to leave, it will tell you why.
Lexmark, which was once the printing arm of IBM, is a $3.7 billion provider of printing, imaging, and software solutions. Lexmark has used a similar strategy to focus on combining printed and unstructured data and integrating and delivering it enterprise-wide. To provide these capabilities to their customers, Lexmark needed to transform its internal processes and solutions. Now Lexmark is offering managed print services and by this is automating some of the most paper-intensive business processes in the corporate world.
Generate tailored recommendations
Understanding product associations is key for any business-to-consumer or business-to-business model. Designing ad campaigns and inventory planning around future recommendation can be a major profit center when executed correctly. Amazon, for example, recommends companion products to all customers during checkout. If a customer buys an electric toothbrush, for example, Amazon recommends the proper batteries or charging station, if sold separately. Other related products, such as toothpaste and dental floss, are also recommended. This has had a huge impact on Amazon’s bottom line.
The company also collects data when a customer looks at an item but does not buy. It can then use this data to re-market the product down the road, resulting in personalized product recommendations tailored to a specific customer.
As Amazon CEO Jeff Bezos will tell you, obsessing over customers is important. The customer experience starts with your company culture and continues on through your customer service team.
Chief marketing officers and chief technology officers are working together now more than ever. By extracting this high-value data, companies can provide a completely personalized experience that engages and drives additional revenue. Whether you use predictive analytics to tailor recommendations, decrease churn, or increase cross-sell opportunities, insight from your customer engagement can provide long-term sustainable competitive advantage.
For more information on how digital transformation is impacting all aspects of high-tech business, please visit here.