In the words of Malcolm Gladwell, marketers in 2020 have finally reached the tipping point where scalable hyper-personalization of marketing activities. This is largely due to the advancements in marketing technology machine learning and artificial intelligence all integrated with one another.
One example is the Westfield Mall in the UK which has cameras outside the center and in all of the public transportation (trains and busses) that chauffeur people to the mall. These are no ordinary security cameras, they are using facial recognition technology to determine the age, sex, and even mood of the shoppers heading to the mall, and based on these factors present the most appropriate advertisement on its many digital billboards located outside and inside of the mall.
As a digital B2B and B2C marketer, I find this very interesting and ponder how these same types of technologies and ideas can be used online to hyper-personalized marketing communications using the combination of internet and mobile technologies.
For the past few years, I’ve been entrenched in personalized email campaigns, cart abandonment email campaigns, Facebook custom audience ads, and other forms of personalized marketing strategies, but so many people are doing these same campaigns they are beginning to lose their luster in the midst of the saturation of these techniques. Think about the recommendation engines that both Amazon and Netflix have built. These engines work so well they have catapulted these companies ahead of the rest because of the way they can personalize your experience so well when you use these platforms. It knows almost exactly what I want to buy or watch next I sometimes think that they are reading my mind. That’s the type of hyper-personalized marketing that all companies need to adopt in some way.
But how can marketers get to this level of personalization? Well, some are. In fact, a recent Gartner study revealed that companies that are investing in online personalization technology are outselling their counterparts by approximately 30%.
The buzzword for 2020 should now be hyper-personalization – the harnessing of all forms of big data used in unison across all marketing channels and customer journey stages. Embracing this approach is going to move customers from top of funnel awareness to post-purchase happiness in record time through higher and more effective engagement at every stage.
Engagio has put together a spectrum of content that marketers currently create, and today we need to move away from the far-right of the spectrum and towards the far-left end of the content spectrum.
So what is the recipe that B2B or B2C digital marketers can follow to enable hyper-personalization? There are three main ingredients:
Engaging customers with hyper-personalized campaigns that customize their experience with your brand or organization. According to recent findings by the Epsilon Group, “80% of consumers claim they are more likely to make a purchase when brands offer personalized experiences.” In this qualitative study, one of the respondents reported hyper-personalization campaigns drove a 3-4x more engagement with the brand. A B2B respondent reported that full-funnel personalization has doubled its webinar and event registrations.
So how do you get this higher engagement and results? This requires software and integrated collection of intent data (ie. web page visits, time spent on those pages, cart abandonment, etc), IP information, cookies, customer identification, and the use of strategically time marketing automation activities. These ingredients can allow you personalize the look of the website, its content, the headers and footers they see when they visit, and the ads they receive that align with the funnel stage and level of intent. All of these strategies will increase the engagement of your website visitors and help move them through the sales funnel quicker than ever.
This is the ingredient that B2B and B2C marketers need to borrow from Netflix and Amazon. It requires truly getting the right message, to the right person, at the right time. This had been spoken about before, but we finally have the tools and knowledge to do it properly by using richer behavioral data and intent data to create messaging that hits each individual’s personal needs and pain points.
If the first two ingredients are added correctly, the third will naturally follow – trust. With so much competition in the online space, customers are going to choose the one that they trust the most. That’s why reviews are so important in every aspect of our lives, whether we’re buying business products or deciding on which restaurant to bring our family to. Aside from customer reviews, good educational content is a prerequisite.
Companies need to invest in an education team that puts out how-to’s, instructionals, thought-leadership content especially in video form. This type of content needs to be delivered to the customer based on their specific needs, intent, and funnel stage. This is necessary for hyper-personalization.
The key to making this recipe work is taking a data-driven approach that is personalized for each account and each person at every touchpoint along the buyer’s journey. If executed properly, it will result in higher engagement, more customers, a larger pipeline, and larger account wins.
Let’s define further some of the terminologies we’ve used so that you can walk away from the article with more than a conceptual approach but an actionable approach.
Customer Content Audit.
Re-evaluate all of your target customers and their needs at every stage of the purchase funnel. Evaluate all of your current content and tag it by the customer persona and funnel stage. Revise any content that will better suit each persona’s need at each funnel stage. Fill in the gaps by creating new content or identifying influencers that you can partner with to create new content for each persona designed to engage them at their current funnel stage and move them along to the next stage of the journey.
This type of data can be collected in a couple of different methods: First-party intent data references how accounts interact with you on your properties while third-party intent data uses the content people are reading on third-party sites to identify which accounts might be actively researching particular solutions.
Intelligence such as sales profiles that are rich in data on target prospective customers and accounts, primarily using manual research methods.