AI is revolutionizing how marketeers and customer-facing business areas are interacting and engaging with the customers. In fact, in today’s fiercely competitive world, data science is helping rewrite the dynamics of the business, as it enables precision in personalization of the customer journey, which was not possible before.
Today, every company’s future is inexorably tied in with the journey of its customers. 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.
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. For personalization to be effective, it requires a systemic and sustained effort and the involvement of all of the team members. Investment in data, tech and people is required to make it a success.
So how can AI help drive this forward?
Personalization AI can help businesses to improve the customer experience, increase sales and revenue, and improve their marketing efforts.
In DAIN Studios we recommend to focus on four major initiatives for deployment of AI and data science to personalization:
- Customer Onboarding optimization: By setting the customers up for long-term use of your product or service from the initial stages with the help of algorithms, we are increasing the retention rate, boosting referrals, and reducing the abandonment rate.
- Next-Best-Action calculation: By using a dynamic decision strategy that uses all customer data to find the best next action for (potential) customers, we are increasing customers satisfaction which will lead to higher conversion rates and revenues.
- Cross-Sell and Upsell of products and services: By recommending products or services that are tailored to a user’s interests, we are increasing the likelihood that a user will make a purchase, which will lead to an increase in revenue.
- Churn prediction and prevention: Based on a dynamic calculation of the percentage of dropped out customers within a predefined time interval and deploying prevention strategies to prevent churn, we are ensuring long-term relationship with the customer and revenues.
The impact of the deployment of personalization AI will be measured in:
Improved customer experience and brand experience!
*Please note that the benchmarks and numbers mentioned in the article are based on internal research and clients projects of DAIN Studios.
Which industries will benefit from personalization AI?
While Personalization AI can be beneficial to a wide range of industries, including e-commerce, manufacturing of consumer and industrial goods, retail, finance, healthcare, and more, specific applications will vary depending on the needs and goals of the individual business.
For example, manufacturers and retail can engage in a direct-to-consumer interaction and use AI to understand the customer needs, recommend the products based on their browsing and purchase history, and hence increase the overall basket value.
In the healthcare industry, personalization AI can be used to provide personalized service, such as by providing information or assistance that is tailored to a customer’s needs. In the finance industry, personalization AI can be used to provide personalized financial advice and recommendations, such as by analyzing a customer’s financial history and providing advice on investment or savings options.
Preparing your business for Personalization AI: How to get started?
Getting started with the Personalisation AI journey means getting the business ready to become data-driven. Whilst all of the following steps will be important, without data, none of it will work.
Getting the data in order to be able to build machine-learning models means centralizing and activating the data. Centralizing data will help get all the data in a high-quality manner into one location, like a CDP. Activating the data means acting on the outputs of the machine-learning model, to derive real, tangible value for the customer and business.
Aside of getting the data in order, there are a number of activities on which the business needs to focus on:
- Identify the specific goals and objectives that the company hopes to achieve with personalization AI. This can include goals such as improving the customer experience, increasing sales and revenue, or improving marketing efforts.
- Collect and activate data about the company’s customers. This can include data about their preferences, behavior, and interests. This data can be used to train the personalization AI and to provide personalized experiences for individual customers.
- Select and implement a personalization AI platform that is suitable for the company’s needs and goals. Specific platform or tool will depend on the company’s needs and goals, while integrating the personalization AI with the company’s existing systems and processes, such as customer relationship management (CRM) systems or marketing automation tools will be the key for success.
- Monitor and evaluate the performance of the personalization AI to ensure that it is achieving the desired goals and objectives. This can involve tracking key metrics, such as customer satisfaction or sales revenue, and making adjustments as necessary to improve the performance of the personalization AI.