Aldi recently stirred up controversy with their attack on loyalty programs and the cost they incur on the average Australian’s pocket. In fact, Adrian Christie, the head of customer service at Aldi, claims it would take $15,000 in grocery spend to have enough points to redeem a frying pan.
This is disputable, and there is a lot of information out there on how to get the value for your points. But at Analytics8, when we first saw this news, we started to think beyond the points and started to question what else Aldi could be sacrificing in not having a loyalty program.
With the push to online shopping and self-service cashiers the traditional relationships between brands and consumers are diminishing.
The concept of the self-service grocery store was first introduced in 1916 by Clarence Saunders in his Piggle Wiggly stores. Prior to this all stores used grocery clerks who would assemble your grocery order for you. This provided each clerk the opportunity to form a relationship with their customers and adjust service to suit individual needs.
Despite the less personal experience at self-service stores, they quickly became the norm as they reduced costs and prices.
In our current age we continue to move further and further into a world of self service, with online shopping in Australia set to reach $3.1 billion by 2019.
As our shopping moves online, old fashioned customer experience doesn’t have to be entirely lost.
With huge numbers of customers shopping across a variety of channels it is often impossible to replicate the clerk/shopper relationship of the 1800s. However, by leveraging the power of advanced analytics, there are ways to ensure customers are still treated as unique people.
Advanced analytics for personalisation at scale
In advanced analytics, personalisation at scale is the intelligent use of data to understand the needs, desires and communication preferences of each customer. When applied to marketing, personalisation enables businesses to deliver the most engaging and relevant content, products and messages at the right time through the best channel.
Traditionally marketing took on a product-centric view. A marketer considered the products they had and decided which customers to market to. With personalisation, a customer’s attributes are considered first. The best product, creative, message, channel and time of day can be selected to suit their unique needs.
For personalisation at scale, data is needed on the individual customer or entity.
A loyalty program is one way to collect attributes on a customer, but the more attributes the better. Beacons can be used to determine when and where a customer shops, cookies can track online behavior, transactional data can help determine what customers are buying and in what quantity.
Provided data can be tied back to an individual or entity it can be used for personalisation.
Once data is collected, advanced analytics can be employed to create a personalised customer journey. From understanding each customer’s needs and drivers to actioning a personalised approach in communication and service. This could mean an offer via email for one customer, an offer triggered when a loyalty card is swiped in store for another and the suppression of offers to a third.
At Analytics8 we use a variety of advanced analytical methodologies to help our clients start personalising their messaging, products and customer journey. Propensity models can be developed to understand the probability of a customer behaving in a certain way. Traditional segmentation models help in understanding different types of customers. Hyper-segmentation creates many small clusters to understand drivers at an even more granular level and can enable an ‘even more’ personal approach.
Finally we use recommendation and next best action engines to help our clients start activating on these models in a data-driven way.
For example, at a bank, a propensity model could predict a customer would be highly likely to respond to a home loan offer and a credit card offer. A segmentation model could indicate that they are most engaged via messages around sales and discounts. Finally, a next-best-action model might determine that a credit card offer would provide best value to the bank for this customer. When the customer next enters a branch and gives their customer id to the teller, or they next login to online banking the customer is offered a new credit card with discounted fees.
If the purpose of personalisation is to rekindle an old fashion customer relationship, it needs to be done in a way that maintains the customer’s trust and protects their rights. This means it cannot be done in the shadows.
When a company is transparent and open about the value exchange that results from using data for personalisation, consumers are more likely to engage with personalised content.
With regulation around the world catching up in the data space it is more important than ever before that personalisation is done legally and respectfully. At Analytics8 we can help you maintain the trust and security of your customers and their data while ensuring personalisation benefits them directly.
Without personalisation, messaging can be irrelevant or just plain wrong.
Generic messaging can be annoying or leave customers disengaged. In fact, a study by Salesforce indicates 62% of consumers expect personalised offers.
I live in the southern hemisphere where we are coming into summer. Nonetheless, I have been inundated with emails from a US retailer I subscribe to, encouraging me to purchase warm winter clothes. These products aren’t irrelevant to me so the emails are annoying!
If this company took a personalised approach to their member base, they could choose the products to promote based on a customer’s location, needs and product preferences.
Recreating the personal connection between the brand and the consumer drives uplift.
In a 2018 study HubSpot found that personalised messaging drove a 202% uplift on generic call-to-actions. This is because personalisation creates a unique customer experience. It shows you know who your customers are and care about their interests.
In contrast, generic messaging can be annoying or wrong, losing the trust of your customers. At Analytics8 we saw one example where a client was only considering averages. However, their data was bi-modally distributed with very few of their customers exhibiting the average result.
If done well, personalisation at scale doesn’t have to be creepy. Models can be built to predict customers who find personalised content invasive. Obvious personalised messaging can then be suppressed to these customers. In this instance, a customer who is wary of personalisation may still get an offer for a travel card coming up to a predicted overseas trip, but a more generic call to action could be used. For example, “Travelling soon? Take up a travel card” could be replaced with “We have the best travel cards for use at home and away.”
Rekindling the customer relationship through personalisation.
Aldi’s Christie asks who benefits from loyalty programs: the customer or the shareholder?
“A loyalty program is great for insights that can manipulate and can help shape the market. But in terms of value, is that value for the customer or for the shareholder?”
If the power of advanced analytics is harnessed through personalisation in a secure, respectful way, then loyalty data can empower a brand to provide each customer with a unique and relevant experience.
Of course, technology can’t replace the one-on-one relationship of the store clerk and the customer, but with personalisation your company can show each customer you care. And, in a world where personal interactions are diminishing, that’s quite nice.
If you’re keen to find out how personalisation can take your business to the next level, please get in touch.