The myriad ways in which consumers interact with brands, grow more complex and sophisticated with each passing year. Digital points of engagement are constantly on the rise and marketers face mounting challenges in understanding, assimilating and then responding to the needs and wants of their customers. Correspondingly, there has been an increase in the number of tools and platforms available to make sense of this data overload. But this just leads to a different problem – siloes of data stacks from multiple vendors. Vendors working on different aspects like website analytics or SMS, are not really concerned with how their data integrates together with customer transactional data and helps marketers make the right customer-centric decisions. Ultimately, marketers are left struggling to gain a composite view of their customers’ behaviour and the ROI of their marketing activities.
An integrated, unified view of customer’s transactional analytics and marketing analytics, helps to align marketing and even business activities to data-driven insights. With such an integrated analytics tool, marketers can easily demonstrate the value that marketing adds to the business. They can also then focus their efforts on the activities that actually matter.
A recent Forrester survey commissioned by Google, unearthed that organisations that link marketing analytics to business results are over three times more likely to hit their goals than other organisations, and nearly 20% of these companies overshot their revenue goals by more than 10%.
Reduce Churn, Increase Cross-Sell, Drive Repeat Customers
So what’s the missing link between several sources of data and business decisions that create year-on-year growth? The answer lies in:
1) Creating or subscribing to data integration services
Data integration is undertaken by a team of experts who are responsible for aggregating data across multiple sources.
2) Analytics capabilities
Analytics is carried out by a team of experts who help make sense out of that data i.e., draw the right insights through analytics.
Today, simply tracking key metrics is not enough. You may be able to acquire a customer or tempt them into a one-time purchase or subscription. But a single-minded focus on acquisition is just not enough to stay competitive in today’s landscape. Acquisition is an expensive and short-sighted business goal. The million dollar question is how to retain customers.
It’s a well-known fact that a significant portion of a business’s revenue comes from repeat customers. For eCommerce firms, 40% of their revenue is created by only 8% of its customers. That means just 8% of your repeat customers are responsible for the lion’s share of your sales. Moreover, businesses with 40% repeat customers generated nearly 50% more revenue than businesses with only 10% repeat customers. Repeat customers also help businesses weather periods of economic instability.
It’s no wonder that the top-most priority for organisations today, centres around reducing their churn rate and increasing cross-sell opportunities. Engagement and re-engagement is the need of the hour and this requires targeted tactics and a way to constantly measure the efficacy of those tactics. An integrated analytics tool can provide the answer to all those questions, and more.
Analytics to the Rescue
The first benefit of a top-line analytics tool is segmentation. With advanced segmentation, you can group together individuals based on their historical behaviour. This allows you to aim targeted marketing activities at them. For example, for a segment of high-value customers, you could consider a loyalty-rewards program to let them know they are valued and important. For customers that are in different geographical regions, you can ensure that they are presented with the best deals and offers before their festive seasons. The options are virtually limitless and marketers can constantly tweak and refine their strategies based on the segment’s behaviour and responses.
A high churn rate is a major alarm signal for an organisation. What it means is that while you were able to acquire a customer or momentarily pique their interest, you failed to follow up on that promise. If a high churn rate is your cross to bear, it’s critical for you to clearly understand the risks and the exact reasons behind it. A predictive churn model can help you design a data-driven retention strategy. It will measure the future risk of a customer ending their relationship with you – whether through an ‘unsubscribe’, by blacklisting your messages, or by simply not buying your products or services anymore. You can then immediately focus your efforts on engaging and retaining these flight-risk customers. To be forewarned is always to be forearmed.
It’s not unusual to see organisations like banks, insurance firms, or even software companies, that provide contractual or long-term services, fall into the all-too-common trap of complacency. They assume that if a paying customer has signed up for their services, their potential has been fulfilled and the case is closed.
This could not be further from the truth! Imagine a customer of an insurance company who has just bought himself a health insurance policy. Every month, he dutifully pays his premiums and the relationship plods along at a comfortable rate for years. During those years, this customer has bought a car, gotten married, had a child, and bought a house. The opportunities are plain to see! This single policy holder could have also been a customer for family insurance, life insurance, car insurance and even home insurance. The relationship between the insurer and insured could have been enriched manifold. The customer could have been nudged and nurtured into buying several more products that are relevant to his station in life. Organizations stand to lose out on many such golden opportunities if they do not adopt and deploy aggressive cross-sell and bundling strategies. With analytics, the insurance company would have been armed with the necessary insights and could have made the most of cross-sell opportunities with this customer.
Along similar lines, any visitor to an eCommerce website is familiar with the message: “You may also like…” Customers love to know that you’re ‘listening’ to them and ready with helpful, personalised suggestions as they browse for what they want. It helps improve the relationship of trust between brand and customer and fuels retention. In fact, a jaw-dropping 35% of retail giant, Amazon’s revenues, comes from recommendations.
So how does it work? Recommendations are generated by a powerful analytics engine that can process and compute a customer’s behaviour in real time. These recommendations need to be generated fast, as the battle for a customer’s attention should not be lost to something else. Recommendations can be made on based on content, i.e., what a customer looked at or bought recently. Recommendations can also be made based on community, i.e., what similar customers have bought or viewed.
With the right recommendation engine in place, your customers have a helpful sales person at their side, enriching their experience and steadily boosting your sales.
Analytics and Beyond
As marketing becomes the driving force of business growth, the onus and pressure is on marketers to deliver. With a single source of truth that combines a customer’s transactional analytics with marketing analytics, marketers have access to a universe of clear and actionable insights. They can then mobilise multiple tools and features to turn those insights into action, and see results.
With analytics, marketers can help their companies plan their investments strategically and make better decisions by deploying the right activities for the right customers. This can help brands realize untold value through reduced churn rates, increased cross-sell opportunities, higher campaign ROI, and ultimately, better decision-making.
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