“The painful reality is that establishing a personal bond with individual customers is hard for omnichannel retailers,” an article for Fourth Source recently opined. “While it’s true that digital retail generates a lot of data relating to individual customers, which can be extracted and utilised both online and in the store, few businesses have successfully struck the balance between helpful and uncomfortable.”
The fine line between cooperative and creepy may mean some organisations push back. For Optimizely, a company based out of San Francisco, their tagline urges companies to ‘be brave, experiment everywhere, and transform customer experience’.
The company provides a variety of tools, from experimentation – A/B and multi-page, across web, mobile and server-side – to using machine learning for recommendations to deliver ROI.
Starting with the basics
Yet the best laid plans will collapse like a house of cards if not built on solid foundations.
“Why I think most companies end up going wrong is that when they step into a process like personalisation, they end up building such a grand vision of all the data that they need, all this data that they have, that they forget there’s a lot of essential basics that they can start with,” explains Hazjier Pourkhalkhali, global lead optimization strategy at Optimizely.
“There are clear places where you can get started and learnings you can use, both for skilling up the organisation and for building a better track record that makes it easier to cooperate and building business cases for larger campaigns that you can run.”
A journey of a thousand miles begins with a single step.
ask yourself: can you identify a group of users yourself today?
“Most companies today need to step away from ‘this is all the possible data that we have, and we’re going to use the most advanced algorithms to figure out exactly what to send to which individual’, and start with a very human-centric challenge,” says Pourkhalkhali. “Ask yourself: can you identify a group of users yourself today, think of a logical way of identifying them, then think of an experience that will be meaningful to them?
“When companies can’t answer this question, it is premature for them to start thinking about their data architecture, and what else they might need,” he adds.
Pourkhalkhali gives an example of a customer in the technology space with a ‘tremendous wealth of products on a variety of systems’. The company identified that this breadth of product was liable to cause confusion among customers.
“For them, there was a very clear use case,” he explains, “and that was to take all of this information that if you have a customer coming in and you know that they’re using your media application on iOS 8, iPhone 6 Plus, that you know exactly who this user is, which content is most relevant to them, and by the stage of their maturity, which technical issues they might be dealing with.
“They were able to reduce a lot of customer issues and reduce a lot of the inbound customer service calls, which was a great cost reduction for them, and also improves customer satisfaction.”
Another talking point is around technological change: is it a hindrance or a benefit? On a related topic to machine learning is chatbots. Pourkhalkhali says they are a ‘very logical’ technology and have the potential to change the way retail works.
Because the average retail website today cannot go through all of the potential questions a customer may have around a product.
the average retail website today cannot go through all of the potential questions a customer may have
“If you are buying a couch, and you walk into a furnisher outlet and you’re going to ask detailed questions – you might be curious what type of wood is involved, how much strain the wood stands, how waterproof it is – these types of queries and concerns aren’t met by most online websites today,” he explains.
“When it comes to complex products, you’re buying technology or you’re buying furniture, you have a lot more discovery that you have to go through and online websites can’t meet the entirety of that journey. It’s inevitable that you’re going to have to walk in to a physical store and personally experience the product, ask further questions, and then you might go back online and finish that purchase.
“That’s where chatbots, and the ability to make drastically more information available to customers, becomes really powerful.”
Pourkhalkhali is speaking at DMWF Expo Global in London on 19-20 June, and customer stories will be on the menu in explaining how to transform experiences in a continually changing landscape of user behaviour.
“Personalisation is this topic that has been built up in such an abstract way that it’s become incomprehensible to people,” he explains. “Marketers have a tendency to talk about one to one personalisation, this idea that we reach this end state where everything is available to every person. But they don’t really know how to make that vision become a reality.”