The Analytics Gap
and How to Solve It

September 11, 2018 | 5 min read

lia-grimberg

Lia Grimberg

AVP Consulting Services

Thought Leadership

Once upon a time (well maybe only a year ago), in a land very close to us, I worked for a company whose name is difficult to mention without getting a strong reaction: Sears Canada.

I will not go into details of what went wrong – I will leave that for another post, but I did want to write about my experience in running a CRM and Loyalty team with respect to analytics.

As very well known, Sears sold its wares through a number of channels: store, catalogue and online. It also had a number of businesses: apparel, beauty, home, travel, optical, photo, flower delivery, etc. The Marketing team was structured by channel: flyer, catalogue, digital (which included display, SEO/SEM and social) and my team (which included email), as well as my favorite partners: the analytics team. Plus, of course, we had the traditional retail departments, which included merchandising, store operations, etc.

What all of this resulted in was all kinds of siloed data. From a customer point of view, if you ever bought something in a catalogue, something online and something in store – you were considered three different customers, unless you linked your loyalty card to a purchase in each channel. The problem was compounded if you ever had something delivered, had an appliance repaired, ordered flowers, bought glasses or travel. You had a unique ID each time. We had no single view of the customer. Don’t get me wrong. Phil Olivieri, our wonderful analytics director, and I tried to stitch together what we could and ended up using hashed credit card information, combined with loyalty data to try to connect the dots, but that was a heavily manual band-aid and bubble gum approach.

Additionally, each team had their own reporting and data visualization tools. I received email information. My counterparts received information about the performance of their own channels. We didn’t receive insights and didn’t share this data with merchandising or store operations. All was siloed.

Why does that matter? In today’s day and age, when customers expect personalized offers and experiences, having siloed data that does not present a single view of the customer across all touchpoints, that does not get analyzed holistically for insights and does not drive actionable next steps will result in customer churn.

If, according to Accenture, 41% of customers claim they would “ditch a company because of lack of personalization and trust”, personalization is the new differentiator and those that do not embrace it, will be left in the dust by the likes of Amazon. Data is the new oil, but only if it is analyzed, insights are derived and data is used – otherwise, it is a waste of the valuable resources.

In my recent conversations with current and former colleagues, I have found that my experience at Sears was unfortunately not unique. We see the following gaps in terms of analytics:

  1. Companies either have a vast amount of data or have a vendor that manages their data and have to pay for access
  2. Consolidation of the data across all the data sources, especially with respect to a single view of the customer
  3. Reporting is available, but is disjointed across the organization and uses many visualization tech solutions, which makes it difficult to consolidate
  4. The ability to come up with insights is light and sharing those insights across the organization is difficult
  5. The biggest issue is coming up with actionable steps to capitalize on the insights and seize the opportunity/close the gap highlighted in the reporting

As such, our main analytics issues are not tech-related, they are human based.

When I joined Relation1, I was blown away by the talent on our analytics team, lead by the incredibly talented Jocelyn Callier. Jocelyn and his team, along with our IT and development team, created a tool called R1.ai, which is a Customer Data Platform. R1.ai combines all sources of data (stated, transactional, behavioural, loyalty and 3rd party), creates a single view of the customer, allows marketers access to a single version of reporting across channels, layers models (such as RFM, engagement, recommendation engine) on top and translates these into personalized communications through any channel (email, web, social media, IM, call centre, in store clientelling, etc.). How is that for powerful?

What’s more is that we can help you solve for the human element, both with insights and recommendations from our amazing consulting team (of which I am a proud member), as well as change management that would help your organization become more data and customer centric, as well as facilitate data sharing and decision making throughout the enterprise.

If you would like to learn more about R1.ai, please reach out to me and I would be more than happy to discuss your needs.

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