What is hyperpersonalized marketing, and how it creates value
Hyperpersonalization of marketing has now become a trend. Read how it works and how companies can leverage it to create value.
Hyperpersonalized marketing has become an industry norm in many industries. It started in B2C, and is now spreading to B2B. It started in online sales and marketing, and is now increasingly used offline. However, when giving presentations and workshops about hyperpersonalized marketing, we often detect misunderstandings. Here is an overview of what is (and what is not) hyperpersonalized marketing, what is the role of AI, and how it creates value for companies.
What is hyperpersonalized marketing
Using data to create the best rule
Some people think that hyperpersonalization consists in sending emails on a client’s birthday, or brochures in the language of a customer, or even remarketing the customer with past visited content. This type of personalization has some limits and is mostly ‘rule based marketing’. The rule can be conceived as : If customer Z has bought clothes for a baby girl and comes back to my website, I will show him clothes for a baby girl or If company X has bought machines in a specific product category, I will send it updates about new machines in the same product category.
Hyperpersonalization is not rule based. It is based on Artificial Intelligence and data, and uses data to define the best possible rule for a single customer. In other words, hyperpersonalization uses data to define what to show or present to a customer. This is sometimes also designated as individualized marketing or predictive marketing. Coming back to our previous example, this will be :
If customer Z has bought clothes for a baby girl and comes back to my website, I will show him clothes for a baby girl but also some complementary products that other similar customers have watched and If company X has bought machines in a specific product category, I will send it updates about new machines in a different product category from which other similar companies are buying products.
Hyperpersonalization in the marketing mix
Thanks to Artificial Intelligence, companies can indeed predict in real time which person or business will be interested in which product or service, at a specific time, specific location. This is why the term 'predictive marketing' can also be used in this context.
Hyperpersonalized marketing can be used in every component of the marketing mix. More and more companies are using hyperpersonalization to provide a fully personalized marketing mix to their customers. For each customer, the content, timing, pricing and marketing channel are personalized.
You can use hyperpersonalization to define the best :
- Content of a recommendation : what to propose or communicate to a customer;
- Timing of a recommendation : when to reach out to a customer;
- Pricing of a recommendation : at what price or what discount to propose a product;
- Marketing channel for a recommendation : through which offline or online channel to reach out to your customer.
How hyperperzonalization creates value
While high volumes of data can be a challenge for marketeers, AI enables you to convert your data into action, in a way that no human could ever do, by predicting in real time or quasi real time what to recommend to which customer. By showing the right message to the right person, marketers can increase the return of their actions in a significant way. No wonder that, according to McKinsey, the value creation of AI in sales and marketing will reach to up to 1,3 trillion dollars in the next 20 years.
In essence, we distinguish 3 main business objectives that hyperpersonalization can help to pursue :
Increase sales and revenue
Hyperpersonalization helps to boost upsell or cross sell. Amazon is known to generate ⅓ of its sales through recommender engines. By showing a relevant product or service to a client, at the right time, you increase the chances of conversion or of increasing the overall basket size. The possibilities in terms of upsell/cross sell are quite large.
Increase customer retention by improving the customer journey
Have you ever heard about ‘serendipity’? Serendipity is this feeling of ‘finding something that you like, by chance’. Netflix is a good example of a successful serendipity experience. Algorithmic serendipity consists in using your data to provide a personalized customer journey, or pushing a specific post to a customer at a specific point of the day. You provide value to the customer, so the customer stays longer with you. If you know about serendipity but have never heard about Algorithmic serendipity before, this is not surprising. This is not only complex but also very new. Kantify is a forerunner in sucesfully implementing algorithmic serendipity.
Develop new services and business models to customers
Some companies use their data to develop new services to their customers. Let’s take the example of Expedia. Expedia leverages its data on hotels, flights etc, to provide to its B2B partners and affiliates some products and services where personalization of the user journey is embedded. For instance, users will see a different picture whether they are a family traveller or a business traveller. In other words, Expedia provides not only a solution, but also a solution with more value, thanks to more data. This is the same principle than for the Facebook ads or the Linkedin ads. But don't think such business models are reserved to tech giants: at Kantify, we have implemented similar business models for smaller size clients operating platforms in other industries such as financial services or marketing.
Hyperpersonalized marketing uses three types of data. First, customers data, or user data. This is data about the customer gender, location, revenue, etc. Second, product or service data. For example, SKU data in the case of retail, location data, sometimes weather data, etc. , Third, data related to the behaviour of customers : app behaviour, clicks, ratings...any kind of data showing an interest or a disinterest for a specific product. Depending on the data that you hold, there are several possible combinations. Sometimes, your data can be used to create new data sources. In a nutshell : with hyperpersonalization, too much data is not a challenge but an asset. The more data, the better, within the limits of GDPR, of course.
The first AI technique used by such solutions is content filtering, where we will use data of one customer to predict what he will be interested in. The second AI technique, used in combination with content filtering, is collaborative filtering. Collaborative filtering uses data from one customer to predict what another customer, with a similar behaviour, will be interested in. Combined, these techniques can help to have relevant, hypertargeted recommendations, also for long tail products or services.
Hyperpersonalization : for whom ?
You may have noticed that hyperpersonalization is more and more hype. This hype is not due to the hype of AI but to the impact of hyperpersonalization of companies revenue. The business benefits of using AI for hyperpersonalization is now more than proven. Hence why some companies chose hyperpersonalization as the first use case of AI in their business.
While hyperpersonalization started in the B2C industry, especially retail, it is more and more expanding to other industries, including B2B. Amazon and Netflix are not the only ones using hyperpersonalized marketing or experience. Kantify has also developed AI based solutions for Belgian SMEs and mid-size companies.
Companies who are considering hyperpersonalization should have sufficient data in terms of products or customers, or acquire such data, sometimes through scraping, sometimes through data acquisition. For a solution to learn and be relevant, data is a must. If you are not sure about whether your data is sufficient feel free to contact us.
If you are B2C and havent considered hyperpersonalization, now is the time
B2C companies have to consider hyperpersonalization as it is becoming what customers expect, and will soon become the industry norm. Depending on the data you hold, you can generate a competitive advantage with hyperpersonalization. If you are not in the capacity to do it now, because of other priorities, you should at least understand how to use it so you make the right decisions now, for example in data collection.
If you are B2B, you have a real card to play
While hyperpersonalization has started in B2C, it is now developing at a fast pace in B2B. B2B companies also hold large amounts of data. They can even easily enrich their data with open, public company data, or develop new business models such as the example of Expedia. In B2B, there are plenty of ways to implement hyperpersonalization, depending on the data you hold : personalized product recommendation, personalized discounts for upsell or cross-sell, etc.
Define your business objectives
Beyond the AI hype, you should be clear on how hyperpersonalization should help grow your business. For example, will you need product recommendation, or rather personalized discounts? If you don’t know how to start, find a partner who has experience and will help you define a few scenarios, or assess how other companies (in the same market or another market) have done it. This is important as you want your investment in time and money to deliver lasting results and ROI. Also, being clear on what you want to achieve in the mid to long run will help you choose the right technology or product.
Want to learn more about hyperpersonalization, serendipity and business value ? Just get in touch !