Playing the long game results in longer term revenue and happy customers who keep returning with their business
As consumers, customers and users of myriad websites, portals and apps, we all demand that our digital experiences are tailored to our interests and needs. We want our favourite websites to deliver products, services and contents quickly; we want to be offered suggestions that will surprise and delight us.
For these retailers, personalisation of their customers’ digital experience is key to their bottom line and credibility. Making content, products and services relevant, useful and interesting, they need to keep their customers engaged, loyal and purchasing their products, services and content.
Delivering a successful personalisation strategy will:
- Drive loyalty, brand engagement and revenue
- Engage end customers with content that is relevant and interesting to them
- Surprise and delight customers with relevant products and services that will help them
- Build loyalty and increase retention through rewarding customers with offers and discounts that will benefit them
- Ensure loyalty and retention, resulting in revenue and growth
In addition to uplift or increased revenue, there are more subtle outcomes than such brand engagement or customer loyalty. Playing the long game – and using personalisation to encourage customer retention – results in longer term revenue and happy customers who keep returning with their business.
It all starts with the data
Personalisation starts with data. The basis for any successful personalisation strategy comes from understanding your customer well, through the analysis of multiple data points.
We can characterise the data available to us as:
- Historical and transaction data – build relationships between customer profile properties and products, services and content using offline batch processes and data science algorithms
- Clickstream – analysing actions and events generated through digital interactions. We can use these to build our customer profile and as a cue for manipulating or personalising content in real time
- Trending and contextual data – incorporating contextual or trending data allows us to understand the context of our customers and add greater relevance to their experiences
In Figure 1, the components of historical, clickstream and contextual/trending data are brought together in the analytics engine. It is this blending of data that allows us to understand what groupings, segments (a defined selection of people such as ‘Females over 30’) or attributes customers belong to so that we can match content to them.
AKQA has delivered numerous successful websites using Adobe Experience Manager (AEM). AEM can deliver personalisation capability through content targeting, which allows a website to display different content to different visitors based on information known about them.
Content targeting works against a set of rules that defines what content is shown to whom. These rules can operate against distinct groups or segments of customers, and the rules can be built up and made more complex against multiple segments until they offer a personalised experience for each customer.
For an online clothes retailer, these rules might be as simple as ‘Show men’s clothes to male customers and show women’s clothes to female customers’. Not the most sophisticated personalisation, but even with this simple example, content is becoming more and more relevant to the customer and is more likely to result in interest, sales or loyal returners.
But content targeting is just this: content targeted to customers based on (potentially complex, but essentially static) rules.
Predictive personalisation and machine learning
‘Machine learning is a critical tool used for gaining actionable insight, more accurate foresight, and relevant inferences into your ever-increasing amount of data. A widespread application of machine learning is the recommendation engine.’ Ted Dunning & Ellen Friedman
AKQA has used a range of tools to deliver sophisticated predictive personalisation and machine-learning moves from rules-based content targeting, to leverage predictive algorithms such as Collaborative Item Filtering to large datasets to predict products and content that will interest customers.
Adobe Target can be paired with AEM to deliver predictive personalisation by building a profile of each customer from collecting data from their digital journey. Target uses algorithms to determine relationships between customers, products and content, and create insight such as customer segments.
Target then acts on this insight to deliver content that is relevant and personalised. The machine-learning component uses testing and evaluation of the recommendations made and their effectiveness in sales conversion – or metrics such as dwell time – to learn which recommendations work and which don’t work and for whom. This results in a self-learning cycle and automatic optimisation of personalised content.
What’s next with personalisation?
‘Organisations need to go further. First by delivering the right experience to the right user at the right time and on the right device; and next by delivering relevant, tailored experiences that meet individual user needs by combining historical, behavioural and profile data with real-time situational feedback. We call this next step in customer experience targeting, contextualisation.’ Forrester
The introduction of context allows organisations to tailor their recommendations of products, services and content not just to the likes, dislikes and customer segments, but also to attributes such as location, weather, digital channel or time of day. These contextual cues can be vital in understanding more about the customer’s intentions. A customer could be walking past a retailer on a hot sunny day – a contextualised offer for their favourite ice cream could be pinged to their mobile phone that they can redeem and benefit from immediately, driving both sales and loyalty.
Don’t be annoying
‘Tailored experience or digital stalking? Has personalisation gone too far? Brands risk alienating consumers with overly invasive social campaigns.’ The Guardian
Predictive analytics and personalisation risks being disconcerting or downright annoying when applied without careful thought. It’s important that care and consideration is used in the personalisation of digital experiences to ensure that:
− Personalised content changes and does not become irrelevant or stale − Content is relevant and rewarding to the customer (there’s no point recommending a lawnmower if the customer is filling up their car) − The customer isn’t overwhelmed or nagged by repetitive messages (messages following them around the internet can be disconcerting and annoying: avoid stalking)
Summing it up
‘Studies have shown that it costs five times as much to find a new customer than it does to keep an existing one. Finding new customers requires spending for advertising, promotions, website maintenance and social media. So understanding what your existing customers want and delivering it to them effectively is the best way to retain customers and grow your business.’ Sage Consulting
Personalisation of digital media can produce significant rewards when delivered carefully and in a targeted manner. Tools like AEM and Target can help produce significant uplift when used as part of a personalised campaign, compared to those that don’t leverage personalisation.
However perhaps one the most powerful and enduring deliverables is that of customer loyalty. Keep customers happy, interested and loyal by personalising what is important to them, and they will become loyal brand advocates who keep coming back. Sometimes it’s the small things – like feeling known and understood – that count.