Today, a company’s future is inexorably tied in with the journey of its customers. But thanks to the content explosion, customer attention has become scattered.
A precious commodity, customer awareness can be acquired and retained with a more individualized marketing plan.
Most of the enterprises, (and maybe yours, too) are creating customer personalization based on what they think a customer wants. This may not match with what your customers are actually looking out for. This is where data science comes in.
To succeed at customer personalization, your brand needs to design a data-driven “Personalized Customer Experience Plan”.
Studies have shown that 88% of U.S. marketers reported seeing measurable improvements due to personalization, and 44% of consumers said they would become repeat buyers after a personalized shopping experience with a company.
What’s more, businesses saw an average increase of 20% in sales when using personalized experiences.
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In fact, customer personalization does not end at selling a product or service to a customer. It has to extend beyond.
Highly personalized customer service can help a brand exceed customer expectations resulting in higher NPS.
This will help reduce churn and upsell/cross-sell opportunities. Upsell-cross sell marketing analytics is one of the most comprehensively discussed concepts in analytics solutions consulting.
A superior level of personalized customer service could include one or more of the following:
Customer loyalty
Studies in the past have shown that over 75% of customers were more likely to continue doing business with brands that offered loyalty programs.
Personalization also has to be at scale, requiring companies to have two intermingling roadmaps – of business and technology.
Personalization is a “means to an end”; it is to meet your marketing goals, which, in turn, are tied in with your business goals.
In their jointly-written paper, “Practical Advice For Achieving Personalization At Scale”, Jeriad Zoghby, global head of personalization, Accenture Interactive, and David J. Neff, VP of Consulting, Clearhead (now a part of Accenture Interactive), say, “Rather than pursuing the outdated principle of “right content, right time, right individual,” the core tenets of “personal” experiences are highly achievable through data-driven experience design principles.”
These experts write that some of their recent findings had shown that consumer expectations were outpacing brands’ efforts to be personal.
Yet, those same consumers were more likely to shop with a brand that treated them in a personal manner.
In fact, nearly all consumers were still more likely to shop with brands that recognized, remembered, and provided them with relevant offers and recommendations.
If personalization has to be described in one sentence, it would be: To improve your brand’s engagement across all points in a customer’s journey.
And, if personalization at scale has to be described in one sentence, it is: To serve up one-on-one offers to individual customers in a very large group.
As management consulting firm McKinsey & Company has spelled it out in a detailed article titled, “A Technology Blueprint For Personalization At Scale”, personalization at scale relies on an organization’s ability to orchestrate the 4Ds—Data, Decisioning, Design, and Distribution.
But before we get to that, let’s talk of the major hurdles personalization at scale faces.
Challenges On The Way
Delivering a personalized customer experience at scale revolves around the classic journalism tactic of gathering information – the 5Ws and 1H, i.e. Who, What, Where, When, Why, and How.
To gather this information, you require tools and data. Tools are emails, flyers, your website, and so on, which, in turn, collect raw data about every individual customer.
Who is the customer segment, What is the messaging, When and Where refer to the channels you need to use to project personalized offers or messages, and the Why, of course, is answered very simply as the need to be doing this?
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Here are some of the biggest hurdles in the path of personalization at scale:
Data in silos: If the raw data around a customer resides in individual containers and are never brought together as a “single source of truth”, forget personalization at scale, even personalization efforts will be hampered.
Regulations: With new laws like the GDPR and The California Consumer Privacy Act being enforced, collecting data has become that much more difficult.
Of course, cookie-based or 3rd party data collection will soon be out, and enterprises will have to rely only on 1st party information. The option of anonymity offered under the new laws will also mean certain personalization efforts may never be fully complete.
Like the McKinsey article says, to combat data being siloed, your enterprise needs to deploy a customer data platform (CDP) to unify data and make it available across all your channels. Express Analytics’ CDP Oyster offers this and more.
Designing offers for individual customers based on their buying patterns calls for using modular content which can be delivered in an agile manner.
According to the McKinsey team, “content must be broken into modular components, for mixing and matching in dynamically populated templates to be delivered in multiple-form factors on the fly.
In order to deliver a consistent experience to your customers through their buying journey, your marketing team needs to use tools and data to decide on the next best action to deliver to the customer based on their profile.
This decision is the hardest and is still said to be evolving in the customer personalization at scale journey.
Delivering the offers or content to the customer at the right time in their buying journey through the very channel he/she is using is the last of the 4Ds. The experience has to be seamless.
Eg: If a customer has viewed a product in an email, thus showing interest, he/she should be “offered” the same on the social media channel they are using. Delivery also must be trigger-based, and the triggers need to be put into place after careful consideration, based on the patterns unveiled by machine learning models from a customer’s shopping habits.
In conclusion: It is clear that customer personalization must become part of a company’s business strategy. For personalization to be effective, it requires a systemic and sustained effort and the involvement of ALL of the enterprise team members.
Investment by way of tech, data, people, time, and money is required to make it a success. Marketing, sales, and other key team leadership and members need to be aligned with the business goals as well as the personalization strategy to make it a success. An “engaged” customer will eventually be a happy customer.