Consumers have more choices than ever before on where to shop, bank, travel, eat, and more. To catch and retain customers’ attention, brand marketers are turning to the power of AI to create highly personalized experiences and offers. These targeted campaigns are data-driven and demonstrate that brands truly understand what their customers want, explains Raviteja Dodda, co-founder and CEO of MoEngage.
Modern consumers have access to infinite information, products, and services just a few taps away on various devices. As privacy laws increase in stringency and device makers give consumers more control, your customers have more opportunities than ever to opt-out of providing personal data. They are also now aware of how valuable their data has become.
In return for sharing their personal information, customers expect a meaningful experience from the brands they know and love. It’s up to brand marketers to rise to create personalized digital and offline experiences that keep consumers delighted and coming back for more. Let’s face it, knowing your customer’s name is not enough anymore. Marketers need to demonstrate with each interaction that they have a well-rounded picture of their target audience.
It all comes down to demonstrating that you truly understand your customer’s needs. Sending irrelevant or repetitive messages reveals to your customers that you don’t know them as well as they thought. Recent research shows that this is the number-one behaviour by brands that annoys customers most. And that’s when they may start shopping around to another provider.
A multichannel experience
Digital channels offer a more personal way of connecting with customers than traditional media like radio and television. Connected consumers access content across multiple devices and from anywhere. Email, text, social media, and mobile apps have replaced traditional in-person touchpoints that once provided brands a chance to create a personalized relationship with customers. We must attempt to recreate that experience through intelligent technology.
Omnichannel engagement gives marketers more opportunities to add value and build trust, and just as many to erode it. Engaging across many channels also generates mountains of customer data that must be protected, sorted, and analyzed. Then, it’s up to the marketing team to decide what to do next based on that data. With customer expectations riding high, brand marketers need to build a unified customer profile, unlock any data silos, and use a holistic view to create precise, effective campaigns.
Ordinarily, identifying these opportunities and aligning the proper channel and message hinged on guesswork and A/B testing with varying results. But today, advances in marketing technology and AI have eliminated the need for costly and time-consuming trial and error campaigns that can alienate your customers. Customer analytics and data-led insights, powered by AI and predictive technology, open a new window into customer engagement that can be finely tuned to be more strategic and deliver meaningful results.
Be In the moment
Data gathering, analysis, and reporting that once took weeks can now be done in minutes without the risk of human error. AI’s insights and customer behaviour predictions allow marketing departments to step away from old models and embrace a new way of building connections directly with customers, using a strategy called moment-based marketing. AI can help marketers determine which message to send at the right time, using the customers’ most preferred communication channels. Finally, technology allows marketers to demonstrate to the customer in the moment that they know what they want.
Getting a complete picture with RFM Predictive Modeling
AI provides a 360° view of the consumer to quickly determine what they need and when. One approach using AI is the RFM predictive model, which stands for Recency, Frequency, and Monetary.
Recency answers the question, “When was the last time your customer made a purchase?” A high recency value means a customer has recently considered your brand for a purchase decision or taken action to buy. Recency can be scored by grading on custom-built filters, such as products or services purchased within a certain number of days (week, month, three months, etc.).
Frequency answers the question, “How often did your customer purchase in a fixed period?” A higher frequency value means a customer buys from your brand frequently and is likely to be a loyalist. To calculate frequency, marketers should analyze the total number of purchases in a fixed time, such as a year or a given shopping season (Back to School, Holiday, etc.).
Monetary answers the question, “How much has your customer spent with your brand so far?” A high monetary value means your customer is one of the highest spending patrons of your brand. Monetary value can be graded and organized using custom-built filters, such as customers who spent more than a specific dollar amount.
Your approach to each customer will vary depending on where they are on the RFM scale. Loyalists who score higher will require a different set of messages than a customer who may be on the verge of abandoning your brand for another. Segmenting your customers based on the RFM predictive model can help you create more personalized campaigns and anticipate with greater accuracy how they will respond to your offers. Remember, each point of contact should bring the customer closer, not push them away. Unfocused and unwanted messages hurt credibility and decay trust, increasing the likelihood of customer churn.
AI does not just help marketing teams recognize opportunities and consumer needs; it creates a solid foundation for developing automated campaigns and launching them with precision. After identifying peak engagement periods, AI has the power to initiate a campaign at the perfect time to maximize response. Instead of lengthy A/B testing, AI learns the success rate of each variant in real-time and adjusts accordingly to drive traffic to the preferred content. Intelligent marketing technology makes creating high-performing campaigns simple.
For example, a multi-bank payment app ran a content optimization campaign with two variants; one featuring an image and another with only text. This test revealed that the variant without the picture (variant 2) performed better and increased traffic to the preferred message. The result was an engagement increase of 21.26% and a greater CTR for the overall campaign.
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Personalization done correctly is based on data and insights. AI helps feed those insights by providing a clearer picture of the customer’s needs and preferences. With data backing every decision, marketers can build strong bonds with modern consumers. The result will be a loyal, long-term customer who will become an evangelist for your products and services. And that is a win for everyone.