The old version of demographic personalization “group-ized” rather than truly “personalized,” lumping people together and sending generalized content to each segment according to expected behavior. Yes, marketing used to be based on guesses. Smart guesses, but still guesses. Today’s personalization is based on actual behavior data. No guessing. You and your teams have real insight and can now implement individualized messaging and action flows without as much guesswork.
Personalization as we know it today is actually hyper-personalization. For example, it’s your sales team and a specific lead with their purchase pattern, benefits sought and placed in the buyer’s journey, or your marketing team and a specific person who has viewed your video before but hasn’t moved to the landing page yet.
Or, as digital technology crosses to the physical world, your retail associate in-store and a customer who may or may not have a profile on your app or website.
Delivering rich relevance in customer experience
Hyper-personalization is the new normal. When Amazon or Netflix shows you recommended products and shows, their algorithms have already looked at your personal data and the data from other users very similar to you.
You use insight based on the recipient’s personal data and behavioral data from others in the same segment to deliver to that specific individual’s needs and preferences.
With every hyper-personalized messaging and marketing tactic, you enhance the individual experience of the recipient. You deliver content to accurately help them and effectively nudge them along the buyer’s journey or the sales funnel.
The combination of individual and segment behavioral data gives you plenty of opportunity to deeply connect with every single one of your customers.
This connection is something they already expect: A customer may not be happy if you offer a holiday promotion on steaks and the customer is vegan. In 1980, that might have been forgivable, but not today. You could run the risk of going viral on social media for making a faux pas.
If, on the other hand, your customer has previously bought dog gear from your camping store and you packed their latest purchase with a thank you card with an adorable pit bull cartoon on it, your customer would love you. That’s rich relevance. That’s hyper personalization made physical.
They might even stick the thank you card on their fridge because it’s so cute, and their guests who also have dogs see your website on that card and you get new customers without even trying.
How to get started on hyper-personalization
AI technology makes hyper personalization easier and simpler, but it’s not instant magic.
1. You need a flexible personalization platform.
When you’re looking for personalization engine technology, look for the following criteria:
You’re looking to use artificial intelligence to process data beyond human abilities–so make sure your AI platform can process, analyze and combine machine learning and deep learning to constantly self-tune and keep looking for and adapting to new information, able to integrate and match your needs and the needs of your target audiences.
- AI that works with human intelligence
Augmented intelligence: This is a happy combination of using AI for personalization and automation at scale, and using human intelligence for reasoning, making judgment calls, and human engagement.
- Ability to scale dynamically
More users, more data. More power, more user-friendliness. You need this scalability. Look for an AI solutions with an interface your sales and marketing teams can actually understand, manipulate, implement and tweak without the need for IT (or much of it).
- Visual, accessible analytics
Speaking of user-friendliness, you want your AI to be easily accessible to your data teams for optimization and fine-tuning as needed. Is your platform integratable or connectable to existing analytics tools and CRM?
- Ability to make sense of intent
This is where your personalization platform helps so much with your marketing and sales teams, in its ability to analyze various contexts and ingested behavior and user data to reach logical and accurate insight on the user intent.
2. Prepare for a company-wide cultural shift.
Once a company incorporates a technology to make hyper-personalization possible, there is no immediate magic. Yes, technology can clean and help make sense of data right away, even providing some immediate actionable insights, but it’s a marathon not a sprint.
The technology has tons of potential in learning, but it has to be given time to learn. That means the personalization engine itself needs to be continuously fine-tuned. It has to be user-friendly and scalable so that your teams can work with it, easily feeding it information on what information to seek and how to handle that information.
New mindsets, new processes will need to be a part of the overall game plan at the company.
Silos will have to be punched through–your sales, marketing (digital and traditional), customer support, fulfillment, and retail teams (online and offline) need to be able to work together to deliver a smooth, omnichannel service to every customer.
3. The “human in the loop” factor is critical.
One person, if not a team of people, needs to be part of vetting the insights the engine produces. This is part of teaching the technology. The expertise of your marketing and sales teams will help decide how to use the insights generated by AI, and how these insights can be part of the strategy that aligns with the company’s overall goals.
Artificial intelligence is powerful, but intelligence and creativity remain with the “tastemakers,” your human teams, who definitely can and will make your AI work for you.
This is how you implement personalization technology that will help you see bottom-line results. Rich relevance in customer experience and hyper-personalization are the results of machine intelligence and human empathy working together seamlessly. That takes time, effort and patience, and the results are worth it, especially in personalizing experiences in the physical world, not just digital.
Personalized experiences across digital and physical worlds
Without being creepy, it’s the cheerful assistance of human teams who start the cycle of voluntary information (zero-party data) that AI feeds on, analyzes, and passes on to human teams.
Cycle 1:
Human: Real agents who answer queries on chat.
Machine: AI which gathers queries from chat and builds dynamic customers profiles on CRM.
Human: Associates who assist shoppers in physical stores in response to their previous queries or interest on chat. Seamless omnichannel service.
Machine: AI which sends personalized message asking for review/feedback on customer’s purchase and assistance received from the sales associate in-store. AI receives feedback and adds it to customer profile on CRM.
Cycle 2:
Human: In-store retail associates who ask walk-in customers if they’ve got the app, and offers to apply discounts on the spot upon download and login.
Machine: AI which gathers data from social logins on the store app, and delivers recommendations and location assistance. AI adds these data to existing customer profiles on CRM.
That kind of personalized, seamless experience is now expected. Brands need a consistent flow of service and support to their customers online and offline. Without talking about the Bluetooth and AI interactive technologies at airports or those employed by giant brands, smaller companies can also benefit from personalization engines–if they know what to look for.
