With an increased focus on what value your tech and professional service provider can deliver, we thought it would be useful to highlight some really big missed opportunities to link the online customer experience to that of in-store behaviour.

According to research by Barclaycard, 85% of customers say that they buy additional items in store when using a click & collect service. That’s not to say that all of your interactions should now be pushed into the store from online BUT there are going to be some very clear and obvious reasons that you might want to complete the journey offline while still attributing value to the part online played of course.

However, before you rush out and pay millions of pounds to an ethically questionable large consulting house to deliver your omni-channel strategy, perhaps consider taking a data-led approach by first using tools that you likely already have access to or can procure/implement without a big team.

Here is our 3 stage approach to handling the site to store opportunity:

STAGE 1 = Triggered Reasons + Link Back

Maybe you’ve already got click and collect, and maybe you track it correctly depending on whether someone purchases the item in advance or purchases it in store… either way, this is still relevant for you.

To warrant a link between the online experience and the store experience you first need a compelling reason beyond the obvious. Examples (triggered reasons) we’re used to seeing are:

  1. DELIVERY – This could be for cost saving reasons or perhaps more likely in 2023 to be due to the absolute shambles of a situation the Royal Mail strikes (and most recently ransomware issues) have caused. This is your typical use-case for click and collect but it is by no means the only reason.
  2. FITTING – Somewhat linked to the above but drawing people into the store to try various different sizes, see how the material feels, walk up and down the store to see if it squeezes your toes are all very good reasons to deliver a prompt. The actual trigger logic could be based on:
    1. % product return rates (ever seen that in a digital analytics data set?)
    2. Most challenging products from a fit POV (e.g. footwear).
    3. Behavioural cues from a customer adding multiple sizes of a product to their basket or even just selecting different products at the Product Details Page before adding to basket (bit of clever JavaScript required to figure this out…not much more).
  3. CHURN – the focus for this is generally to always get someone to buy something but should it be? If you are able to see or predict any type of churn, why not add a component to see how pushing them into store helps? Examples could be:
    1. They’ve been on a few product details pages and then go to exit the site (when their cursor move off the page)… you know the one I mean. It’s where you normally get given a ‘please don’t leave, have 20% off message’ from a partner like Yieldify. Why you’d pay them for sales coming through this means is beyond me given how easy exit pop ups are to deliver. I actually think one of our developers could do it with their eyes closed – not a joke.
    2. You have a data science team (maybe of 1… doesn’t matter) that has been able to apply regression-based learning on the data and identify specific customers and/or behavioural traits that would indicate someone is going to churn. There’s a good chance this won’t include store data because it can be complex to link together but often this is because people are overthinking it striving for some perfect Single Customer View. We’re not ignoring that, it’s just that we’d consider that more of a Phase 3 or 4 kind of thing.
  4. LOCATION – Admittedly this one can be a little hit and miss although that can be addressed if you can compel the customer to tell you where they actually are. IP addresses are not great at giving you a true location. It will in most cases give you the right country, in a decent number of cases give you the approximate area (city for example) but beyond that I definitely wouldn’t bother. Examples could be:
    1. It’s absolutely freezing cold and raining in Birmingham (surprising I know) and my IP suggests I am nearby. Why not suggest the customer head over to the nice warm store to look at the winter clothing available from cool new stuff down to clearance end of line? If it’s any other day than Tuesday to Thursday there’s also a good chance I’m at home and not at work and might like a short break in my day – got to look for the positive nuggets post-pandemic..!
  5. PERSONALISATION (per’son-al-i-sa’tion) –
    1. Clear Preference – If your customer chose click and collect the last two times they ordered from you and both of those orders were picked up from the sale postcode area and you’re not personalising the experience with this on the product details page, someone (or multiple people) should get fired. The point here is that this one is such an easy win and it’s really not that difficult to do.
    2. Prediction – Taking the Churn example above and applying some additional logic to it such that it works a bit more dynamically based on exhibited real-time behaviours vs manual regression modelling and this can be done in a way that predicts when someone isn’t going to make an order online but might in store. This doesn’t have to be done via click-and-collect. The message could be delivered at any point to the user upon qualification into the dynamic audience. Sounds a bit like science fiction… it is very much not.

I know what you’re thinking by now… ‘Great examples Steve, you’ve cleary got your head screwed on right and could possibly pursue a career in comedy… but how do I actually go about linking the journeys without having to invest in loads of development time or technology licenses?’. Thanks, luckily I have thought about that one and have some experiences I can share.

Let’s address the core issue – linking online and offline. Ultimately if you can’t do this then you don’t stand a chance of being able to say whether this site to store effort was worth it. Luckily for you, it’s not all that complicated. You will need:

  1. A digital analytics system that uses an anonymous ID as a key (Google Analytics or Adobe Analytics would work).
  2. For the above system to also ingest data from external sources either in real-time or batch format (still GA or AA).
  3. Some sort of reservation ID system in store. This could be similar to a click and collect system you have or something more simplistic like a queueing system you used to get at the butcher’s back in the 90s (some supermarkets still have those… pretty sure it’s for nostalgic people like me).
  4. Willing and competent developers.
  5. A professional services partner that actually knows what they’re doing… or at least isn’t going to charge you for the hours they spend fumbling around trying to make it work before they find one of our articles and pass it off as their own expertise.

Note that I’m unlikely to have mentioned any technology that you don’t already have. All we need is a binding key and a bit of creativity. The basic flow would be:

  • User hits the site
  • Trigger message shown (for one of the reasons given above)
  • An ID is generated by the website and by the analytics tool
  • The analytics tool ID and website/app ID is given to the back-end (IDs linked)
  • User arrives in store and presents ID generated by website/app
  • User purchases item(s) and all website/app ID logged as a field against purchases
  • API request made to the analytics system pushing transaction event data and links based on the IDs stored (hinged off analytics ID)

Yes, I am simplifying the approach and there are various specifics one needs to account for depending on what point-of-sale system is used and which specific analytics tool is used but actually these are relatively minor compared to the level of insight this can open up for a business.

Realistically, the above could be delivered within 3 months and a professional services budget of approx £15,000-£50,000*. I normally see quotes with at least one extra 0 on it for this sort of stuff.

*If you’re reading this and it’s not 2023 please consider what inflation has done since 2023. If we continue at 2022 pace then you might have to add an extra 0 on by now. If Putin and Brexit had a lovechild it would actually be Satan.

And that’s it… that’s Stage 1. No super expensive tech, no massively lengthy development cycles and we can start to join up the customer experience online and offline in a useful way for the customer whilst also very likely (not guaranteed) increasing order conversion rates. So what’s next?

STAGE 2 = Single Customer Profile

The Stage 1 example is great when the journey starts online and finishes offline but in reality many journeys are not that linear. They involve multiple touch-points over a period of time and often involve multiple devices as well. How the heck do we handle this?

Regardless of this known challenge, we would likely still recommend to any business to complete Stage 1 first. It is a relatively low-investment method of understanding more about the relationship between online and offline for your brand and will likely give you some solid foundational data for determining whether you should invest in more technology/people/processes to handle more complex reporting needs. Furthermore, the work done during Stage 1 will not be regret work – much of it can be used for Stage 2.

As the title suggests, Stage 2 involves building a single customer profile of all engagement events regardless of where or when they occur. The system needs to be flexible enough to handle multiple methods of identifying an individual as well as handle events collated with timestamps that occurred in the past. This is really where the concept of a Customer Data Platform (CDP) starts to make a lot of sense to businesses – build or buy. Let’s be clear about how unclear a CDP might be though. CDPs come in many different flavours, the most common of which we at DMPG understand to be as follows:

  • Activation = Using the single profile that is enriched in real-time or near real-time the main focus is to build audiences based on specific behaviours and then activate these across a number of digital touchpoints.
    • EXAMPLE = Tealium Audience Stream. It is possible to do more with the Audience Stream data but ultimately this requires exporting it into a business intelligence tool to manipulate and model.
  • Journey Orchestration = As per above but additionally provides a very rich set of tools to ensure the entire digital journey is orchestrated and managed from a single platform. This includes building a rules-based workflow along with connecting directly into activation tools like email, SMS, on-site messaging etc.
    • EXAMPLE = Bloomreach CDXP (formerly Exponea). The focus here is to provide a more encompassing set of tools to better manage the end-to-end digital customer experience as opposed to integrating with a wide array of other tools like Tealium Audience Stream is designed to do.
  • Data Consolidation = Provides audience activation capability and in some cases a level of orchestration workflows but really the primary focus is on the customer profile and handling multiple complex datasets to be able to create that unified profile which can then be activated either in the tool or through integrations with others.
    • EXAMPLE = Treasure Data. This kind of system starts to make more sense for complex (often enterprise) organisations that have lots of disparate datasets some of which can conform to a specific schema and some that can’t. These tools provide a rich canvas of applications that allow you to ingest, map, model and create unified customer profiles.
  • Platforms = Designed to provide a complete platform of tools to achieve the same outcome that all of the above might provide if combined. Hypothetically a one-stop-shop for all your CDP, data modelling and journey orchestration needs.
    • EXAMPLE = Adobe Experience Platform (AA, CJA, AT, AC, RTCDP, AJO, AEM). As far as I know, Adobe are the only provider of a truly complete platform that could provide a one-stop-shop option.

I could spend the next 20 hours writing about CDPs but Will Taplin is in the process of writing a series of articles digging into the specifics of CDPs and the importance of use-case planning before investing. You can find the first article here and get your full geek on as you please. For the purpose of this article however I will just provide summary detail and high-level value outcomes.

So, for what reasons might a CDP now provide additional functionality for Site to Store data integration?

  1. MULTI-STAGE JOURNEYS – This is where a customer moves between online and offline more than once to complete a journey. For example, maybe they actually start in-store while on a shopping trip. They buy product A (and maybe register for a loyalty account) and then head home. Their email is in the CRM and as such start receiving emails based on other products that might be relevant to the first product they purchased. Now, this in itself does not need a CDP… this is CRM basics but you might be surprised how often this sequence does not happen or certainly not with any form of personalisation. Anyway, let’s not get into that here. The customer clicks on product B from the email, looks at it online but is unsure about whether the product will fit or perhaps they want to buy it for their husband and talk to someone about the various colour options. They need to come in-store and are encouraged to make a reservation to speak to someone in-store. They arrive in store but decide that product C is actually better suited to who they are buying for. However, the correct size is actually out of stock in that store. They decide to complete the order online now as the size is available from the central warehouse to be delivered to their home. They arrive home and complete the order. The journey has now been:Store (product A) > Email > Online (product B) > Store (product B) > Store (product C) > Online (product C)To stand any chance of being able to understand the above flow requires appropriate technology specifically designed to allow events to be recorded against a single customer profile and then be correlated with all other behaviour exhibited for that profile. But a CDP will allow us to do all this right…not necessarily – more on this shortly.
  2. REAL-TIME ACTIVATION – Now that we have all of the multi-stage journey data available to use (not the same thing as a pretty insights report) we’re likely going to want to make use of this in real-time for the following reasons:
    1. Conversion Messaging (personalisation again) – if we know someone is interested in a product and has looked at it online a few times we can retarget that person within our digital advertising campaigns to see if we can complete the conversion (online or offline).
    2. Suppression Efficiency – if we know that someone did finally buy a product in-store after starting their journey online then maybe we want to avoid pushing them the same sort of ‘new customer’ type messaging we use within our paid search campaigns. Why give more money to Google… surely they have enough of it and we can put some of this budget back towards our CRM nurturing programme being much smarter about how we talk to our customers.
    3. Account Growth – maybe called ‘growth hacking’ by people who probably don’t actually know what they’re doing, we could use the data help by our CDP to create a concept of a ‘desired profile’. If we then share attributes about these desired profiles with the likes of Google and Facebook (yes I know Google is changing the way it handles this soon) then it can allow us to more efficiently target the types of people who might have a higher propensity to buy and be high value customers for us.
  3. CUSTOMER EXPERIENCE – Remember that quote right at the beginning of the article? Well, here’s another one:According to the 2022 findings, customer engagement, i.e., attitudinal and behavioral engagement, are significantly correlated with the shopping environment, shopping procedure, product experience, and staff service experience. According to the study, customer engagement has a significant positive relationship with customer loyalty.See https://www.frontiersin.org/articles/10.3389/fpsyg.2022.897851/full for full details of the research study.In short though, the better the overall customer experience is (not just online) the more likely a customer will become a loyal one (i.e. spending more over time). Over the 13 years I have been consulting in this industry, this is probably the #1 goal of about 80% of the businesses I have worked with. Striving towards improving the customer experience therefore should always be a key shared objective by any team, be it the paid acquisition team, retention team, call-center staff or in-store assistants. If you let short-term revenue get in the way of this and derail the overall direction of the business, don’t be surprised when you’re publishing profit warnings.

If you’re now looking for more content about how to deploy a CDP and why it might not give you all the reporting insights you thought it might (as referenced above) then please turn your attention to Will’s article series about this. This article is simply to discuss the benefits and methods at a high level:

Article 1
Article 2
Article 3
Article 4

STAGE 3 = Getting people into store

What I’m about to write is probably going to seem like a cop-out… but the reason why you might get someone into store is really brand strategy and performance use-case specific. If you think it’s because I’ve got bored of writing, don’t. I’m on a 7 hour flight to Toronto right now and don’t actually have anything better to do. The point to understand here is that getting someone into a store when you also have a digital experience is something you really do need to think through and understand the full impact of before engaging in a strategy. Stages 1 and 2 are specifically focused on taking a progressive approach to understanding the customer’s behaviour and also what the implications of your actions are when trying to link their journey up. Quite what impact this has in terms of the overall customer experience and profit of the business is something to monitor and be smart about. You might find that you have 37 core customer audiences based on combinations of their behaviour, cohort, source, interests etc. It may also be the case that each of those 37 audiences respond differently to your efforts getting them back online or into store.

Having the right data, tools, people and process in place is what you will need as part of driving a successful targeted marketing and communications strategy. We have only touched on the first two in this article but will certainly address the second two in due course.

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