Big Data. Trending topic in the marketing arena for some years now. Encouraged by an industry that are making a nice buck, Big Data is supposed to be the goose that lays the golden eggs. Their credo: Big Data builds future-proof marketing. Which, at this moment, is at least questionable. Why focus on Big (= 3rd party) Data when you are sitting on your own pot of gold, i.e. small (= 1st party) data? There is no doubt about the unmistakable value of 1st party data. Working with your own data allows for making serious steps in rendering your customers their personalized brand experience. Just see how it worked out supply chain management and marketing.
Data-driven business pays off
That is what I found out back in 1992 when working for IBM’s International Logistics Center (ILC). Our mission: 100% customer satisfaction among our EMEA clientele through timely order information and delivery. A 200 headcount venture with people gaining their information from separate systems of all sorts. “There’s got to be an easier way”, were my first thoughts. So, after office hours, I took the time to dive into the world of databases and queries, building reports to make my own life easier. After 3 months it had grown into a management information system enabling us to manage by exception. With all of my colleagues getting their specific reports in their mailboxes overnight. Just imagine what valuable information that yielded and, consequently, what huge time savings. From that moment onwards we spent that time on raising customer service levels. This aroused my appetite for more.
Divestment of loss-making activities
Activity-based costing was the next project: measuring the time employees needed for individual activities in daily operations. Recently, ILC gained, with a lot of effort, a contract for product distribution in a specific market segment. An alleged cash cow. The activity-based costing project revealed, however, that these activities did not make a positive contribution margin. Instead, they were loss-making. The divestment decision was easily made.
Surge in shipping accuracy
The next step I made was visualizing the incoming and outgoing supply chain. A report I made, explained crystal clear that our suppliers’ shipment accuracy left a lot to be desired. This was the trigger to renegotiate all service level agreements. The result: an average inventory reduction by 35%, equaling structural cost savings in the amount of tens of millions of US Dollars. A welcome ‘present’ in a market with short-cycled product introductions resulting in obsolete products with, consequently, huge inventory write-offs. It also resulted in a surge in meeting customer expectations by 38%.
Optimization in transportation costs
The last example from the supply chain practice: analyzing the transportation method for purchase orders, I proved that, based on the volume/weight ratio, there ís a preferred shipping method. Above a specific ratio, it is cheaper to ship products by air instead of ocean. The result: yearly structural cost savings of millions of US Dollars. A nice side-effect was a considerable reduction of the total number of expedited purchase orders with related high transportation costs through the implementation of the ‘responsible party pays’ principle.
All in all, this was ample proof that meaningful insights retrieved from 1st party data in one’s own systems allow for making a tremendous improvement with regards to competitive edge and yield. So, if this works in supply chain management why not in marketing?
Fruit salad or ‘apples vs. oranges’
Years later, we talk 2005, I worked as an account director/strategist for a couple of car brands on a pan-European scale. CRM par excellence. Activities included strategic advice, omnichannel model introduction campaigns, brand activations and the implementation of the technical integrated CRM infrastructure. With a Customer Data Platform, hosting 360° degree customer profiles, as the centerpiece to fuel all customer-oriented communications. We worked, of course, in a triangle with the client and the media agency. What struck us somewhere along the way that the campaign results the media agency reported did not match with the data we saw in our own Customer Data Platform.
I advised my clients to orchestrate their analytics activities in-house. We implemented Adobe SiteCatalyst. A true relief: no more discussions about what figures being correct. Knowing that all analytics were based on the exact same definitions. No longer an indefinable fruit salad but being able to compare apples and oranges. And reaping the benefits of the tech’s capabilities to put targets on individual channels while measuring results (i.e. channel attribution). This allowed us to re-allocate budgets and pursue conversion rate optimization.
From insights to relevance
Some 10 years later I worked in a similar role for one of the big DIY players, where our main activities focused on email marketing. A powerful medium with the capabilities to make a substantial contribution to brick and mortar as well as web shop sales. Supposed that you make use of all of the capabilities and tailor content to the preferences and behavior of individual customers. To optimize email marketing activities, I proposed an analysis of the database. We connected email behavioral data to online and offline cash register transactions through a personal ID.
What struck us: as much as 40% of their turnover was promotion-driven! The analysis also provided valuable insights in combinations of products purchased, seasonality, day in the week and time per day. At which specific store, both online and offline. This is all valuable information as an input to make your email marketing efforts more decisive and create more relevance on an individual customer level. Unmistakably the value of 1st party data.
From crystal ball to data-driven predictions
Geese that lay golden eggs may exist in fairy tales, in real life they do not. Despite what software vendors make you believe. There is no such thing as a comprehensive software solution that covers all your marketing needs. Hence, for Tommy Hilfiger, the choice for a technological best-of-breed solution was easily made: a Customer Data Platform connecting data in their existing system and data infrastructure.
What started in 2007 with email newsletters send-outs (with insights in open and click behavior as well as specific interests) has grown into the omnichannel The Hilfiger Club loyalty program. One of the first steps was adding purchase behavior in their web shops and physical stores. Which provided interesting insights in historical apparel preferences. And a basis to adapt the content in email newsletters and online marketing campaigns to reflect individual customers’ preferences.
From that moment onwards we added some 20 additional data sources, thus creating rich and relevant customer profiles. Think data from a personal shopper app, data from customer satisfaction questionnaires and data gathered from a product recommendation engine. All 1st party data. This enabled us to tailor campaigns even more to individual preferences and needs. Based upon insights in socio-demographic preferences, omnichannel purchase behavior and frequency (RFM), purchase behavior throughout time and interests in specific types of customer events.
This provided us with the ‘tools’ to develop different types of campaigns, tuned to specific, actual customer behavior. Such as onboarding programs, activation, and churn campaigns. Also, we are able to reward individual customers based upon their purchase behavior and brand engagement with special promotions and treats, such as VIP pre-sales and invitations for member-only events. A step that has brought awesome results: well over 7 mio (the standings early 2019) active members that spend on average almost 50% more than customers who did not register for The Hilfiger Club program!
Recently, we implemented AI-based predictive models that feed event-driven campaigns and ongoing communication programs on a day-to-day basis. Thus bringing interesting insights: a clear view of customer lifetime value (CLV) helping us to give individual customers an even better brand experience. But we can also predict when leads are inclined to make their first purchase. When existing customers are likely to make their next purchase within a specific category. Or the chance for customers to return their purchase.
The latter insight may be used to implement a business rule that shows shipping costs in the web shop check-out process for those customers who frequently return their purchases. We also used an algorithm to narrow down dozens of communication target groups to 4 distinct personas. This helped Tommy Hilfiger to realize marketing cost savings. And they are capable of tuning their product suggestions, promotions, and invitations even better to individual customer needs.
In short: we created a look into the future that allows Tommy Hilfiger to be increasingly relevant for individual customers. Connecting data from the mobile app – again 1st party data – is the next step in the evolution of Tommy Hilfiger as a love brand. Of course, we utilize 3rd party data where it adds value. We enrich customer profiles with selected data points from the DMP. And target look-a-likes of Tommy’s most loyal customers. In other words, spend the online advertising budget as efficiently as possible.
Shift focus from 3rd to 1st party data
Despite a huge amount of attention for trending themes as Big Data and DMPs, I want to make a case for shifting focus from 3rd to 1st party data. Not only for reasons that your own data is more reliable than 3rd party data. But also for the tremendous value you will be able to create for your customers. Value created when combining data from online and offline data sources to build that single 360 degree customer view.
Turn words into action and shift to 1st gear. With a focus on 1st party data, you will be able to make valuable progress in creating brand preference among existing and in the end new customers. Rewards will be yours. That’s a promise. 1st Party data has unmistakable value!
(this article was previously published on marketingfacts.nl)
