Acquiring customer data and sustaining customer loyalty for long periods is a never-ending challenge that every business faces. While most consumers are members of more than 12 customer loyalty programmes, they are active in less than 50% of those programmes. What makes some programmes more successful than others and how do you make your customer loyalty programme one of those that people use, share and talk about?
At Collinson we work tirelessly with customers to unlock the magic within their business, to design build and deliver the best loyalty programmes that set them apart from their competition and drive desired change – more mindshare, wallet share, advocacy, and Loyalty.
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To learn what we’ve done for businesses like yours and how we can leverage Salesforce loyalty management technology across Customer 360 to help you achieve your customer vision and bring your loyalty strategy to life, please get in touch.
It’s widely reported that the pandemic accelerated the shift to digital shopping by roughly five years, yet we don’t often hear about the accompanying increase in consumer expectations when it comes to online and offline customer experiences. Whether it is retailers ‘show-rooming’ by carrying minimal stock in-store and expecting customers to visit but then buy online, or the reverse ‘web-rooming’ where customers research online and come into store for a final ‘touch look’ before purchasing, digital channels are now a key part of a multi-layered customer experience. In response, brands such as Gucci have launched virtual shopping experiences in ‘Gucci Live’ and other companies are offering private shopping in the homes of their high-value audiences.
Collinson
For loyalty success, the customer experience is an even more important consideration compared to when simply just selling products, as customer expectations are highest for the brands they have spent the most time, money and effort building an relationship with. As with all good relationships they want to see their favourite brands sending the love back in the form of a relevant, value-added and ideally exciting set of interactions. The minimum expectation is a seamless, frictionless ‘swan like’ journey, while higher level expectations include being surprised and delighted in a way that is engaging and keeps them coming back for more.
Data plays a key part in all of this but so does treating the customer as a human, with human interactions and understanding their emotional, not just behavioural, needs. So, it’s not surprising that it takes a lot of moving parts behind the scenes to get ‘it’ right and very few, if any, brands ever really do to the extent that their most loyal customers might expect.
As we have said before, a loyalty programme is a complex machine with many moving parts working together to deliver the above the surface experience. Breaking down this experience into its dynamic components, understanding options and balancing them carefully in consideration is all part of the engineering task in not just building a loyalty programme but operating it to maximum efficiency and effectiveness.
Customers want the value added experiences that were launched during the pandemic to stay even when the world is back to ‘normal’, having realised that these experiences are in fact better than what they had previously. They expect home delivery, curbside collection and digital payments to continue.
When it comes to loyalty, customers have even higher expectations of their interactions with the brands they love, shop at most often and with whom they have consciously shared their personal data.
Unfortunately for the customer the reality can be quite different and often at the mercy of a company’s internal siloes. Data is usually separated and not shared across the business or frontline staff and as such, frontline systems have very little opportunity to tailor the loyal customer’s experience even if they have perfect customer data. At other times, a loyalty programme may resemble a swan swimming, serenely, gracefully, and effortlessly on the surface, but below the surface the mechanics are furiously trying to keep up the pace, frequently using manual workarounds on legacy technology to connect and deliver on the customer journey in line with the brand promise.
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Customer relationships built over a lifetime & destroyed in an instant
Jeff Bezos
We see our customers as invited guests to a party, and we are hosts. It’s our job every day to make every day to make every important aspect of the customer experience a little bit better
Profile vs Persona based interactions
Customer segment level marketing relies on data-mined customer profiles, which ideally use behavioural data to create groups topped up with attitudinal research and even third-party data customer profile data enhancement to create pen portrait type descriptive pictures of different customer audiences and how they behave and to an extent how they think.
From a loyalty programme perspective these segments could sit within defined tiers or even cut across them like in the case of Mothers and Babies content and benefits that are not directly linked to the value of the customer i.e. how tiers are normally derived.
What statistical analysis struggles to handle however are non-trackable attitudes and customer emotions, which also directly impact their needs in any specific customer journey. While it is possible from research, more than data perhaps, to understand customers’ mindsets with approaches like ‘jobs to do’ or ‘customer missions’, actually understanding customers’ needs, which may change during a single journey, is much harder when it comes to planning and executing the best customer journey interaction.
In fact, a single customer, may have different personas and needs in the same customer journey. Consider an airline passenger who is packing at home and needs a checklist of what to take. While in the taxi she is stressed about traffic and needs to see a target ETA at the airport. Going through check-in and security she is nervous in case something goes wrong with the paperwork or process, but as soon as she is into the terminal, she relaxes into leisure mode to shop, dine or relax in the lounge, before boarding the flight and pulling out the laptop as back into work mode. This same customer had very different mind sets or personas throughout this single journey so to say their profile would for example suggest an exciting duty-free offer might be correct, but if it arrives when they are stressed at home packing it would miss the persona target.
Profile vs Persona based interactions
Realtime vs historic data usage in customer interactions
Typically, customer journey intervention planning has been based on data-mined insights from tracked past behaviours, going to great lengths to harvest, analyse and generate deep insights on customers in a fascinating element of marketing science.
These insights, with the help of statistical analysis techniques, can look for similar patterns of behaviour against which regression analysis can be used to work out propensity for a customer to respond to certain triggers and therefore deriving their assumed needs from this approach. Added into the loyalty customer experience this can mean estimating when a customer might shop next, what they might want to buy then, or other sales promotions type customer purchase patterns and subsequent actions.
However, tracking, analysing connecting and using all this ‘big data’, even in the aggregated form of a pre-defined customer profile is difficult for many organisations. This in turn makes it hard to manage and execute data driven strategies at the spend needed for customer experience management, versus perhaps marketing content targeting where the time to plan and respond to data inputs is much greater.
Instead, companies would be well served in customer experience management specifically to consider limited, fast, easily shared and indicative ‘smart data’ as an alternative to or to complement a big data approach. Smart data still requires considerable retro data analysis but what it is looking for are the one or two data points that are part of a customer’s journey that can accurately anticipate customer needs right now, i.e., during the current experience the customer is having.
Realtime vs historic data usage in customer interactions
In terms of defining overall end-to-end customer experience, a key question for many brands is where should it start and where it should end? Since the pandemic, there’s been an increase in new remote operation and delivery business models like click and collect services, where customers can buy online and pick up their order in a different local store. These new customer experience propositions often rely on partnerships (especially in the case of gig-economy type delivery services) which are more efficient than setting up a new service in-house. For loyalty, most programmes run with a selection of third-party partners for offers or points earning and redeeming. Programmes effectively endorse these partners by default, and so create a customer experience risk.
These questions are particularly relevant to those who participate in coalition loyalty programmes, like for example, the fuel retailer BP, which has been in and out of different loyalty alliances and most recently decided to (re)launch its own BPme Rewards.
Controlled end-to-end journeys vs facilitated experience via partner ecosystems
Personalised vs Customised experiences
Mass personalisation has been the goal of data driven customer interactions as data capabilities and customer data collection has increased - this often being one of the key goals and outcomes of having a loyalty programme in the first place. However, removing irrelevant messaging is now just a hygiene factor in customer interaction and customers are desperate for new, exciting, and engaging content and customer experience interactions, as brands seek to deliver WOW as a differentiator.
We are not just talking about anticipating basket items to drive a discount or offer to stretch the customers Average Transaction Value (ATV), but messaging that is unexpected, interactions that create interest and excitement and in themselves are ‘remarkable’ i.e. the customer loves them so much that they want to tell others about them, the utopian goal of customer experience. So, we need to understand not just historic data-tracked behaviours but current customer state of mind, their emotional needs and how to track and respond to them and perhaps here relying on tracked data is insufficient.
Let’s look at a different model for perspective, one that is designed with the sole purpose to engage an audience – video games. These do indeed use customer interaction data tracking to serve up the next challenge or level for the player. However, in most cases they have an exceptionally high level of user-customisation options, where they encourage the player to make choices on how they want to play. The games have a sandbox model where pre-determinedlayercharacteristics, avatars, game challenges, etc. can be set and managed by the user. The sandbox is a way of presenting a number of customisation choices as a pre-defined set rather than have the player try to customise every single dynamic variable possible in the game. A player might complete a game one way, then play again in a different way with a different character or switch between personas and avatars whenever they feel like it. By comparison, most loyalty programmes are linear in terms of the pre-defined path for the customer experience.
Personalised
vs Customised experiences
Machine vs human interface interfaces
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Here are five aspects to consider on how to achieve a balanced approach to defining
and delivering a target customer experience for your loyalty programme
Machine vs human interface interfaces
From long-standing Intelligent Voice Recognition (IVR) filtering your phone call, to more recent AI-driven chatbots, and algorithm-driven engines that personalise content and offers at a one-to-one level, machines have never been more effective at listening to, and understanding, customers.
Machine Learning (ML) and the subset of Artificial Intelligence (AI) is already prevalent today in many customer interactions such as Amazon’s Alexa, or Google suggesting corrections to your search terms. When deployed correctly, these tools facilitate a greater level of personalised and curated customer experience.
Checkout-less stores, as pioneered by Amazon, are currently wowing customers around the world. They use a mix of in-store product and customer monitoring sensors, plus a pre-existing customer relationship for payment purposes, but also machines that watch customers and track them to generate actionable data. That data is then processed and analysed to see what can be altered in order to positively enhance the customers’ next transaction or interaction with the brand, whether on or offline.
However, people like people and loyalty is a relationship. Research tells us one of the most often quoted customer needs is the ability to talk to someone i.e., a human (or at least a pseudo-human) when needed. In fact, 25% of consumers say human interaction before or during the buying process is extremely important to them.
A balance is needed between the efficiency of the machines and the value of human interaction. Businesses will need to figure out how to provide artificial intelligence with human oversight and a personal touch, to connect the complex loyalty machine below the surface with the seamless and valued added experience loyalty members expect.
Take the upmarket retailer Hackett and its 65B Members Club loyalty programme for instance. The company has equipped its staff with handheld devices in store which help identify customers and bring up their relevant history, preferences and critically also recommendations, while enabling the staff on the ground to give the best human contact and data informed experience to its repeat customers.
Roblox, the open world metaverse game, is loved by its young players for the freedoms it allows them to explore, create and communicate, including of course to express themselves – despite being a blocky pixelated experience. Roblox earned $990m in revenue in its full year report for 2020, and $509m in just the third quarter of 2021. The game is just one of the new open world type environments that includes Fortnite and also Decentraland, Sandbox and Somnium for older audiences, to name a few of the new crop of metaverse spaces where some brands are already engaging consumers with unbounded experiences.
Here loyalty programmes have a lot to learn about what makes an engaging and customisable member experience and how that is fundamentally different to data driven personalisation, and perhaps should look to open world games and even metaverse type environments to see how to deliver WOW – by listening hard, following fast and enabling customers to make their own choices.
We see there can be a blurred line between defining data driven personalised and user defined customised journeys, but to be truly relevant and add value, and an engaging WOW experience, loyalty programmes need to understand and deliver value from the combination of the two where:
- Personalisation – using collected and derived data insights to predict and recommend content and experiences based on what we think will best suit the customer, typically using historic data sets of first party data
- Customisation – changes made by the user, after they have adopted profiles, set their own filters and selected views themselves. Customised works from the zero-party data they declare themselves on likes and dislikes, selections, and choices
It’s important to understand this difference. With personalisation, the brand is in charge through its tools and data mining techniques, which might meet the needs for older, more mature audiences. But with customisation, the customer is in charge, provided we give them the option to select and choose. This is the native game type experience that Millennials, Gen Z and of course Gen Alpha – the customers of tomorrow – will expect.
Where the service is mission-critical for the customer, there should be a high level of control and direction from your brand, for example, Emirates Airlines provide a branded chauffer service for free to high value airline customers. But what about earning points with an unrelated third-party, or delivering a customer benefit like a free drink or piece of cake from a high street coffee shop in the case of a retail bank loyalty programme benefit?
Increasingly customers are expecting better things from loyalty experiences than just collecting or redeeming points at a third-party partner. In the case of recognition benefits, for example, when it comes to tier-based experiential rewards for higher value customers their experience will directly reflect their feelings towards your brand, whether good or bad.
A useful halfway point between in-house control and outsourced loyalty customer experience is to use customers themselves to track and monitor the third parties by proactively asking them to feedback on the service they received.
Even partner ratings, rankings and socially shared reviews will help make sure that only good actors who consistently deliver the expected customer experience remain as part of your overall loyalty ecosystem
Controlled end-to-end journeys versus facilitated experience via partner ecosystems
Smart data, tracked and managed on a near real-time basis across an enterprise’s various departments or loyalty ecosystem of partners is much easier to track, manage and respond to than big data and might have almost all the same level of customer needs predictability. Also, from a customer perspective seeing an almost immediate response to an action could add tremendous value. For example, after earning a certain level of loyalty points or rewards in a store, an offer of a free coffee at a nearby coffee shop.
As with all points raised in this discussion there is a need for balance and to combine big data to understand customers and smart data to spot and react to their current interactions. Both have value to add and, in some cases, simple, instant smart data-based responses might have greater customer impact and WOW than a pre-planned historic data driven offer or recommendation.
Personas are much harder to map, understand, recognise, and respond to than profiles, but if used correctly can generate a greater customer response as they are emotionally not just behaviourally aligned to the customer and creating a great customer experience for a loyalty programme needs to consider when to use which approach to generate the best customer response
Again, you’ll need to find a balance between using customer profiles and / or anticipating the needs of their current personas. Profiles are great for holding a lot of data and make planning more relevant customer journeys easier. But personas are much better at indicating a customer’s current needs, based on their state of mind allowing you to respond and react. Just ensure you’re identifying and using the correct data markers and acting accordingly.
Smart data, tracked and managed on a near real-time basis across an enterprise’s various departments or loyalty ecosystem of partners is much easier to track, manage and respond to than big data and might have almost all the same level of customer needs predictability. Also, from a customer perspective seeing an almost immediate response to an action could add tremendous value. For example, after earning a certain level of loyalty points or rewards in a store, an offer of a free coffee at a nearby coffee shop.
As with all points raised in this discussion there is a need for balance and to combine big data to understand customers and smart data to spot and react to their current interactions. Both have value to add and, in some cases, simple, instant smart data-based responses might have greater customer impact and WOW than a pre-planned historic data driven offer or recommendation.
Experience
Which parts of the brand experience will you let someone else control, with
their brand, at the risk of ceding your relationship power to that brand?
If you’re outsourcing how far should you go to enforcing
brand and customer service standards with your partners?
Which part of the end-to-end customer experience should
your brand or loyalty programme control in-house?
The questions here are:
Many components make up the customer experience
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Awny Elafghany
Head of Sales - Middle East & Africa
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