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In a commodity based market, enterprises are searching for ways to differentiate themselves from their competition. One of the opportunities lies in offering customers an excellent customer experience across all your channels. But once all enterprises in a certain market start to offer excellent customer experiences you need to start searching for a new way to differentiate yourself from the competition. I believe future-focused companies will turn to advanced data analytics to create a personality profile for every customer and use that profile to tailor the experience for them.
While a lot of companies are still striving to offer smooth customer experiences, there are some companies that are already beyond that. These companies in the vanguard of customer experience continuously raise the standard of excellence. To quote IBM’s Paul Papas: “The last, best experience that anyone has anywhere becomes the minimum expectation for the experiences they want everywhere”.
Large and unwieldy enterprises are not able to respond to customer demands quick enough, paving the way for disruptive start-ups. That makes it an unfair battle, especially if you realise that these standards in expectations are different for every customer. Customer expectations will have risen dozens of times by the time large enterprises are finally able to meet today’s demand. In other words, the best experience today will be outdated by the time it’s released by an enterprise. This requires companies to look ahead as far as they can.
Looking ahead I believe there is an opportunity for enterprises to start offering tailored customer experiences. Customers are paying for your product or service, but will no longer accept that you create one single experience to fit all, no matter how smooth it runs. News websites for example often use the same headlines for every visitor, with help of personality profiles they could tailor the headlines to what the visitor is really interested in. But this is not limited to online experiences, by implementing personality profiles in CRM systems for example companies will be able to connect customers to call center agents that fit their personality best. Companies should be able to tailor the experience they offer to the needs of every single customer, through all of their channels.
To do this you need to know exactly who your customer is. You need to be able to start capturing the ‘personalities’ of all your customers and translate that into experiences that speak to them personally. This used to be an impossible task because of limitations in technology, but new advanced analytics like IBM’s Watson bring us a step closer to creating customer specific experiences. Watson Personality Insights for example is able to extract cognitive and social characteristics from text that a person generates through tweets, forum posts or e-mail.
As an experiment, take a column or blog written by a well known person, I took Johan Cruyff. Copy a few of his columns (the more content, the more specific the outcome) into Watson personality insights and let it analyze the input. You will receive insight into the writer’s personality right away, and you will find that it is actually quite a solid reflection of what the writer is like.
Surely the beginning will be hard. A lot of companies probably don’t have sufficient data to create personality profiles that really differentiate customers from one another. That’s why high retention rates and customer loyalty are key. The longer you are able to sustain engagement with your customer, the more data you will be able to collect, resulting into more insights and more specific personality profiles.
There are multiple ways to retrieve this data, here are just some examples: Using personality insights on tweets, forum posts or e-mails aimed towards the company. Use a combination of speech to text and tone of voice analyser when a customer is calling the company’s contact centers or brick-and-mortar stores. Analyse Twitter historical data to see if a customer has tweeted about your company and if they did, in what way? You can even analyse tweets that aren’t specifically about your company to extract some personality insights.
To start this process you can create personas for all the different types of customers you have in your company. These personas will be specified with scores for different personality characteristics that are available within the Big Five personality model. The standard big five model should be expanded with some additional parameters such as digital maturity and demographic data for example. Next you can start to assign all your customers to one of these personas and adjust the experiences you offer based on the personas you created. After a certain period of time, with help of advanced data analytics, you can let go of the default personas and start tailoring them to every customers personality traits. For example Johan Cruyff scored 79% on self-discipline and a 100% on authority-challenging. Watson can predict customer needs based on personality characteristics. Johan Cruyff has a specific need for ideals, curiosity and challenge. You can use these needs to start adjusting the experiences you offer by using statements like: if curiosity > 80% & digital maturity < 20%, show extra information alert. This offers a wide range of possibilities like adjusting the tone of voice based on age or changing the headlines on news websites for every visitor.
Following this path will enable companies to offer the right experience to the right customer. Tech savvy teenagers could be met in a very different way compared to the average elderly people that need a lot more guidance and confirmation. Customers might be a bit shivery about the fact that companies will be able to map their personality and surely they would have to agree to some terms, but in the end it will help them having a better experience with companies. Personally I think adoption will depend a lot on the average digital maturity of your customers. A lot of companies are already collecting data around their users, this is just a smarter and more extensive way of processing and applying that data.