All retailers can take lessons from eBay’s personalised customer marketing campaigns.
For the modern-day marketer, the concept of the right message to the right person at the right time may seem like an over-used cliche; a pipe-dream, considering the mountain of tasks and campaigns.
As customers and channels expand, this challenge will only continue to grow unless one can invest (not just financially but intelligently) in marketing automation solutions that will support a customer experience that blends a parallel between business driven marketing (i.e. brand led) with customer driven marketing (i.e. customer led).
Customer-driven marketing has continuously demonstrated higher engagement rates as recipients are receiving messages that are relevant to their own interactions. This strategy is not new though, with eBay being one of the best examples of a retailer’s excellent use of marketing automation with personalisation.
For many years now, eBay (which most of us have probably used at some point) have run thousands of customer-driven campaigns every single day. These campaigns run automatically, with relevant content for each individual customer based on their individual interaction. eBay is one of my favorite examples of a personalised customer experience; the e-tailer curates information to your needs, such as serving you listings of a similar product, or sending you listings of items you’ve been watching for. What eBay has done perfectly is to focus on the key stages in its customer lifecycle that it needs to manage. At these specific customer touch-points, it makes sure the experience is as relevant for each customer as possible.
In 2016, Software as a Service has evolved to make this personalised customer experience easily achievable through three key steps:
- Mine the machine: data is king and sits at the epicenter of marketing automation. Data collection is essential for marketers, but which data is the most relevant for your business? Begin by thinking of the customer experience you want to achieve, the criteria that is needed and then the data required to fulfill this criteria. If it doesn’t already exist, how can you get this data?
- Predict the potential: data without context is meaningless, but contextualising data must lead to a conclusion: in most cases, an understanding of the customer and how to drive success. What have you learnt from this data and how will these conclusions drive customer impactful actions? What can you anticipate from this data analysis and how can you manage your objectives based on this? The data analysis should also be clear on the stages in the customer lifecycle to focus on.
- Evolve the execution: having understood the data, the customer and the stages in the customer lifecycle to focus on, it’s time to act. Which campaigns should you send and how should you manage content? Can a 1-2-many message work or should it be 1-2-1? Furthermore, which are the most relevant channels for this execution? A personalised omni-channel customer experience can be utopian but which channels are your customers expecting engagement on?
When implemented with marketing automation, engagement rates for relevant customer driven campaigns can be 10 times those of regular newsletters. By implementing these across the customer lifecycle, particularly at the most critical stages, marketers can ensure that they are providing customers with an experience that is most likely to drive conversions. Conversions are not necessarily purchases but may mean positive brand interactions.
In a more recent case, Spotify not only created a weekly data-driven personalised playlist through its Discover Weekly, but ran automated campaigns based on customer affinity. So if you can’t get enough of Justin Bieber, and he releases a new remix of Sorry, you’ll be the first to know.
The right message sent to the right person at the right time, but a time-consuming task that can only be achieved efficiently by leveraging marketing technology.
