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LEVERAGING REAL-TIME CUSTOMER DATA TO BUILD CUSTOMER LOYALTY

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ccording to a recent study by Harvard Business Review Analytic Services, 70% of enterprises have increased spending on real-time customer analytics in the last year and 58% have seen an increase in customer loyalty as a result. Clearly, customer analytics is essential for improving customer experiences across marketing, sales, and service channels.

Building customer loyalty is no longer about the product or service. Instead, it now rests on offering end-to-end customer experiences with a view to engage. Starbucks owes a large part of its success to its investment in cross-channel marketing campaigns. By personalizing interactions with its customer base, Starbucks has been able to create deeper customer loyalty than its competitors. Sample this – the Starbucks Rewards Program grew 28% y-o-y to 10.4 million members in the US in 2015.

Why is building customer loyalty so important? Because it not only improves the chances of customers staying with your brand and repeating their purchase but also turns them into word-of-mouth evangelists as they share their positive experiences with others. Loyalty programs must be effective in the sense that they serve a real need for the customer, this can be achieved by fusing data into your retention strategies. Whatever be the industry, organizations today generate huge amount of customer data that can be leveraged to improve loyalty programs by taking action on the insights thus generated.

Leveraging Data to Build Loyalty

The building block of customer loyalty is one-to-one interaction with individuals. A deep customer insight is important to predict consumer behavior at any point in time. This can be achieved by analyzing granular data. The right marketing technology matrix must form the basis of such an analysis:

  • Data collection: To support loyalty programs, data must be sufficiently granular and comprehensive across channels and platforms. Data from the websites, email and advertising campaigns, mobile interactions, social media etc. should be collected.

    To overcome the challenge of data silos across platforms and devices, platforms like Azure Data Lake can be leveraged to collect data across social, mobile and offline channels. Data Lake can be the single source of truth for enterprises and analytics can be run seamlessly on it by leveraging Spark or Hive.

  • Integration of data: Once data has been collected to run a loyalty program, a unified source of information about the customer is required. The information layer, that unifies and standardizes data collected from all sources is a must have to enable data to be analyzed and acted upon in real time.

    In a large shopping complex, data points are frequently collected from hundreds of sensors. Since low energy devices are not powerful enough to run TCP networking stack, protocols like ZigBee are leveraged to send the data to a central gateway that is capable of aggregating the data points and ingesting them into the system. The gateway pushes the data set to an Apache Kafka cluster, where the data takes multiple paths.

    Metrics such as product availability and ROI on promotional offers are analyzed after collecting the data over a period of time. These data points that are collected and analyzed through a batch process. A MapReduce job may be run within a Hadoop cluster for analyzing the campaign effectiveness.

  • Building consumer profiles: Marketers can create a universal user profile that combines individual data from visitor sessions across multiple channels. This can then be continuously updated in real time. Imagine an airline sending same set of marketing promotions across all its audience without looking into the history of where a person has travelled and what their preferences are. The return on these campaigns will be minimal. If that same airline is able to mesh its user data from across devices and sessions it will have a more comprehensive view of its customer behavior. By integrating data from tools like Salesforce and Sabre into Hadoop and running campaigns through Eloqua a corporation will be able to build a better customer loyalty and increase campaign effectiveness.
  • Proactive analytics: By leveraging their analytic capabilities, corporations can build data models on historical real time data and amalgamate insights on customers and channelize marketing programs.
  • Contextual interactions By aligning a customer real-time insights with data models a corporation can predict where a customer is in a journey digitally or physically, helping the corporation draw the customer into subsequent actions that it wants the consumer to pursue.

Personalized Rewards

With personalized rewards, you give your customers what they want, also indicating to them that you are truly invested in their experience. It can be done by sending a simple email survey about the types of incentives that excite them or a social media campaign to identify prizes that make your customers’ lives better. A focus on maintaining positive relationships should be the foundational stone for loyalty programs. Brands can offer rewards based on transactions, shopping frequency, survey responses etc. What’s more, reward programs can be used to capture additional data as customers grant permission to collect data in exchange for something of value.

By analyzing customer behavior in real time through app notifications and beacons, customer loyalty can be built. Today’s technology infrastructure helps us to do that seamlessly. Gone are the days where the only way to analyze customer behavior was in data marts built in Informatica. With real time personalization using tools like Tune and Flurry this can be easily achieved.

Targeted Product Recommendations

Research suggests that customers actively engaged in a loyalty program are 90% more likely to repeat their purchase and five times more likely to choose the brand in the future. Sending targeted product recommendations is an excellent way to keep customers engaged. Marketers should integrate real-time purchase data with historical data to make specific recommendations. Consumer data such as demographics, lifestyle, products purchased by category and type, frequency of purchase and purchase value must be analyzed to create highly targeted product recommendation offers. Gather multiple data points to make intelligent recommendations and turn customers into loyal ones.

Summarizing

Marketing technology today is all about leveraging data such that customers engage with a brand wherever and whichever platform they are on. At a time when it’s easier than ever for customers to switch preferences, loyalty is not easily gained. To earn their trust, data is the most powerful tool in the hands of marketers.