How D2C Companies Can Use Data & Analytics To Improve Customer Engagement

Measuring the depth of customer interactions

By one industry-derived definition, the term “Customer engagement” pertains to the ongoing interactions between company and customer. Typically, it is the company that offers the opportunity to interact with them and the customer chooses the model and dialogue. The richness of letting the customer lead the dialogue lies in the information they choose to share: what matters to them, how they define value, why they feel emotionally invested in your brand.

Going a little more technical, customer engagement is an estimate of the degree and depth of customer interaction against a company’s pre-defined set of goals.

What are the benefits of increasing customer engagement?

There are multiple benefits to increasing customer engagement in eCommerce. The first thing to understand here is that the longer the engagement, the broader as well as deeper the relationship, the greater the benefits.

This is also backed by the numbers: customer engagement research indicates that companies who have made an effort to increase customer engagement cross-sell and upsell revenue by 22% and 13-51% respectively. Their order sizes have correspondingly gone up from 5% to an impressive 85%.

It is great to acquire new customers who followed you for a while on social media before making a tentative purchase. The goal of engaging this segment is to build their confidence in your brand, to take them from “just trying it out” to a conversation, and possibly, advocacy as well.

Having said that, engaging those customers who have made repeat purchases and given multiple referrals on your site is a different and highly important ball game. These are your high-yield customers, your business’s lifeblood. And it is crucial to make them feel appreciated.

The role of data and analytics: Why we need structured data?

When it comes to getting to know your customers in this digital age, data and analytics are the mother lode. It is no longer sufficient to collect and collate customer information in your customer resource management (CRM) software without having any way to make sense of it.

In the digital age, data pours in from multiple sources: Comments, shares, videos about your product on social media, emails, and calls to your customer support, reviews on your eCommerce website, and so on.

This data dump is unstructured and largely meaningless if you lack analytical tools to find buyer behavioral patterns, extract actionable insights, and discover strategies for customer engagement.

Customer engagement strategy insights are one of the most valuable outputs of a dedicated data analytics program. Every digital business, therefore, should have one. You can’t get where you want if you don’t know what your customers are thinking. And you won’t know what to say to them that will help them form an emotional connection with your brand.

Using data to make decisions: Examples of customer engagement analysis

• Behavior analysis of buyers/ visitors to your website- how much time do they spend? Where do they drop off? What are they searching for just before they drop off? These are some questions worth investigating.

• Guest checkout rate: number of customers who use the guest checkout feature that allows them to purchase without creating an account with your site. This matters because if a customer does create an account, they are more likely to return.

• Purchase frequency of a customer: How often a customer makes a purchase is relatable to how engaged they are.

• Repeat purchase rate and order value: The average order value is higher for repeat visitors rather than first-timers. These are metrics you can use to measure the success of your engagement campaigns targeted at bringing in repeat business and increasing CLV.

The biggest strategic benefits of customer engagement analytics are:

• Being able to craft precise marketing messages or push notifications that go out to the right customers, on the right device or channel, at the right time.

• Empowering sales and marketing personnel to refine their pitches in order to better guide the conversation.

• Offering a timely resolution of issues and retaining the customer’s faith in your brand.

• Helping you design rewards and loyalty programs as part of customer retention strategies.

• Gaining a competitive edge and thus differentiating yourself beyond the sales transaction alone.

Sources of customer data

There is no one form of analytics that hits every nail on the head. The biggest value lies in utilizing a combination of analytics tools and multi-channel feedback platforms to get a well-rounded picture of the business and its relationship with its customers.

In other words, the more the data sources and the more comprehensive the toolkit, the better the insights.

The most common customer-related analytics types include:

Customer analytics: An analytics platform that supports customer behavior analytics and helps the company build a picture of customer needs and wants, their preferred mode of interaction, the preferred device to shop on, etc.

Predictive analytics: Tools that sift through historical customer behavior data to predict future customer behavior as well as business results. They analyze the data points to estimate the likelihood of a customer buying a particular product.

Text, speech, and sentiment analytics: Tools that analyze textual (email, newsletters, social media) as well as voice (call/IVR) interactions between customers and the company. They look for events such as the number of positive or negative mentions through social media channels or online customer communities. They screen for typical and recurring problems or concerns, basically, the kind of social buzz their brand is generating. They also look for correlations between services, recommendations, push notifications and other messaging along with the buyer decisions made after these interactions. They also measure the impact of negative word-of-mouth on social media.

Video analytics: This is more for brick-and-mortar stores that analyze in-store video recordings to determine actions and activities that are indicative of buyer decision points and customer experiences.

Established KPIs are also put in place to measure the impact of various customer engagement activities. Furthermore, correlating data insights with customer profiles and demographics helps devise customer engagement strategies across every kind of consumer segment.

Data and Analytics help the company get a 360-degree view of the customer journey across various interconnected touchpoints.

All data-derived insights, good or bad, are ultimately useful. Optimizing the customer engagement process based on data and analytics basically boils down to two sets of activities: Devise the right marketing, personalization, and messaging strategies and harness available technology to execute them. Then, just wait for the results to pour in!