Creating loyalty and reducing churn by implementing Customer Analytics

Today, customer loyalty has become a major focus for all the brands. It’s more about offering customers something as personalized as possible so that they feel truly special. In the present globalized world, the competition is high, and customers have a variety of options available. In such a scenario, the customer experience becomes a huge differentiator. A good understanding of your customers is the key to delivering such an experience. That is where customer loyalty analytics can be very useful. With the help of big data, you can learn about your buyers and tailor that into an experience that matches their expectations. Customer loyalty analytics can also be useful in developing customer-centric marketing strategies that give a higher return on investment.

Analytics that enable shoppers to see what top brands, categories, and keywords are currently trending. This new way of browsing gives shoppers an engaging way to discover unique finds, hot items, and debut brands.

Building Blocks for customer loyalty

Consumers are smart, and they know what they want. They also know that if one business doesn’t provide it for them, there are likely many others will, with just another click. The crowded marketplace means that customers are less likely to remain loyal to one brand, and the advent of social media has also empowered consumers to share both positive and negative brand experiences online. E-commerce and the use of smart devices have provided consumers with a variety of options. Businesses pay attention to their customers’ needs, and it is a drawback to those who aren’t using customer data to empower their marketing strategy. If you’re not collecting and utilizing customer data in your efforts or are unsure of how to use analytics to boost your customer lifetime value (CLV), here are some ideas that could enable a better outcome.

Utilize Data: The Smart Way

Marketers know now that the key to creating more relevant, “sticky” campaigns is to use data to inform your content. But the utility of data analytics goes beyond just the campaign level. Today’s laser focus on customer experience and journey mean that data should be the backbone of your entire business strategy.
Few businesses know how to use data analytics to improve their experience and garner true customer loyalty. In a study published by McKinsey, businesses’ use of analytics to create a competitive edge in customer experience is only at 32%. This demonstrates the issues that many businesses are having in trying to apply data in a universal, transactional way across their entire organization.
Great customer experiences lead to higher retention rates, increased brand loyalty, and bigger customer lifetime value (CLV). Improving customer experiences can seem like a straightforward task, but unless you base new techniques and strategies on tools like zero-party data, you might be putting in effort and resources in the wrong places.
So, what are some RIGHT ways to use data analytics to improve customer loyalty? Here are 5 ideas that could help you get started to build that data-driven competitive edge.

1. Segment Customers: The Smart Way

Today’s ultra-connected world means that both your current and new customers will interact with your brand through many different channels, but eventually, they will develop a preferred way in which they obtain the goods and services they need from you. These preferences provide valuable insights about themselves, their behaviors, their lifestyles, and even their future purchases and areas of need.
By using a CDP (Customer data platform), one can segment the users based on these various touchpoint behaviors on autopilot.
If it’s an e-commerce store, an ‘integrated marketing portal’ will segment the patrons based on their transaction history, the number of previous purchases, and even their product search history, which can provide you with insight that informs a potential path for each customer.
For example, consider the potential implications of someone who “purchased X product” 12 months ago but then also looked at the upgraded version of the product and downloaded the whitepaper on the upgrade using an email link sent to them. You can use these touchpoints to know exactly what branded campaigns to send them, and through which channel (email) to which they’re most likely to respond. You can also infer their areas of interest and what other product and service knowledge will be beneficial for them. These types of data segmentation not only leads to increased sales, but also make the customer feel special and help them in their search for the right product.

2. Fostering Leads

Once data is used to gather information on the customers’ interests, pain points, and preferences, the next step is to consider sharing non-promotional content that’s meant to nurture them through the know-like-trust cycle. The more relevant the content, the more likely they are to remain on your outreach list and will convert into a customer.
For example, a marketing agency that provides SEO services to companies is looking to increase their standing on SERPs (Search engine result page). Some of your clients want to just improve their site’s overall SEO, while others are looking to go deeper and boost the backlinks to their sites.
By collecting the right data, one can provide the right expert advice to each group. The overall SEO segment would get informative blog posts or video walkthroughs about various SEO-boosting website tactics they can use to maximize their potential. The backlinks group would receive tips on prospecting sites for pitching ideas or articles on how to submit their products and services to influencers. Gathering the right data as an asset that is invested in your customers, and that builds trust and loyalty.

3. Personalize

The best aspects of data analytics are how it can help you predict the future. By curating and analyzing data on your customers’ past purchases, wish lists, and even search information, you can provide personalized recommendations which makes them feel that you are truly vested in their brand experience.
Just how important are personalized recommendations to the modern consumer? You can find any OTT platform or eCommerce site that will analyze your choice and provide you with recommendations. For example, you watched a horror movie 3/6 times, the recommendations will come up based on your choice and get personalized. Similarly, you purchase a full sleeve shirt, and a specific size next, the recommendation will be based on your preference and size that is more obvious. These help customers feel special. Personalization of the product is based on the content and taste of the buyer.
Using the data to gain insight into customer preferences and suggest products and services that make their lives better demonstrates a brand that gets exactly what customer experience is all about, and this builds a bridge of loyalty that keeps customers coming back for more.

4. Use AI To Personalize Results

With AI learning, businesses can provide personalized search results to leads and customers faster and easier. Because everything is done in the background and your AI results get smarter with more exposure, you eliminate the need for manual data mining and analysis, saving a lot of time and effort.
For example, many retail tourism companies use AI learning to personalize search results and recommendations. Many website search pages deliver instant search results for returning users based on wish-listed destinations and experiences, previously booked tours, and even recent search history and preferences to keep these results highly relevant.
The personalized search results have already been influenced by the price points, specific amenities, and desired locations these users searched for in the past—so users never get recommendations that are too far out of their budget, needs, and preferences.
Nothing is more frustrating in the age of instant gratification than having to re-enter all the information for which you’re searching repeatedly. This is a smart way of using AI to demonstrate that you know your customer’s time is precious, and you respect that, and that mutual respect is a cornerstone of trust in any relationship.

5. Real-time customer engagement

If the sales processes are streamlined by engaging customers in real-time proactively based on the intelligence of the data sets available, one can significantly speed up your sales cycles and retain customers.
Example: After an online shopper adds products from the store to their wishlist, a smart customer loyalty platform can send them a reminder notification within a few hours, reminding them of that dress they liked so much but this time, attaching a coupon for the dress, making it an offer they are much less likely to refuse. A similar example also helps in reducing abandoned carts. Along with sales, seeking feedback, which not only provides one with more marketing touchpoints, it also shows customers that follows-up with their patrons and care about their feedback. When you demonstrate that your customer’s voices and opinions matter, they’re much more likely to make you their brand of choice.

Thought Leadership