Analytics Capability Development at European Home Improvements Retailer
The Challenge
The Digital Transformation programme at the retailer included the development of a new digital platform to provide products and services to serve customers better throughout their complex journey for high value, high consideration purchases. As the Data and Analytics Lead for the digital transformation programme my challenge was to build a data and analytics capability in terms of the people, technologies and processes as well as implementing a data driven approach to digital product development within the client’s London digital Hub.
The Approach
Setting the strategy
From the very start I tied the data and analytics vision and strategy to the overall digital transformation strategy. The vision for the new digital platform was all about delivering superior customer experiences, therefore the Data and Analytics strategy had to reflect that but also recognise that those experiences would be delivered across multiple (digital and physical) channels over a period of time.
It became clear to me that there would be a number of key components required in the data and analytics strategy:
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A Customer Recognition Framework to provide the ability to understand user behaviour across time and channels in complex multi-touch journeys
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GDPR compliance built in from the ground up
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Scalable and flexible infrastructure to enable advanced analytics at the most granular level as data volumes grew
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Focus on ‘data as a service’ to ensure that all stakeholders had timely access to relevant data and insights
Building the team
I assembled a multi-disciplinary team using the ‘pod’ principle to build the analytics capability in the Digital Hub and to deliver services to the various stakeholder groups such as the product teams, leadership, marketing, content and service delivery operating within a scaled agile environment (SAFe).
A number of roles were assumed by different individuals to create complimentary and overlapping skill sets. In addition we focussed on developing analytics skills within the core product teams to establish a ‘hub and spoke’ approach.
Building the tech
To support the strategy and objectives I adopted a cloud first technology approach using Google Cloud Platform (GCP) to provide cost-effective scalability. The first step was to land the granular clickstream digital data from Google Analytics and create a user centric view with customer recognition principles built in from the start.
Over time, a series of data pipelines were built to land additional data sources into GCP either in batch or in real time before being ingested into Big Query. Once the data was landed in Big Query at its most granular level it went through a series of transformation processes to aggregate and normalise the data and make it ready for reporting and analytics.
Creating the right KPIs
Before the new services were launched I worked with the Venture Service Owners (VSOs) and Product Owners (POs) to create a measurement framework to identify the key stakeholder groups and define the appropriate KPIs and metrics for each of them.
In the early days of a product’s life dashboards provided early indicators of performance with more sophisticated analytics being delivered once data volumes had grown.
Creating the Insights
As new products and services were launched the initial focus was on tracking their performance. Dashboards were created in Datastudio using the KPI Measurement Framework that had been developed for each service. The emphasis was on self- service reporting with additional analytics provided by my Analytics Team.
As the services scaled and data volumes grew, the focus switched to the use of advanced analytics and data science techniques
In one example we established which user behaviours were the most predictive of someone registering and creating an account on the service. Registrations was a core KPI as the act of registering was a key concept in the Customer Recognition Framework and understand customer journeys over time and channel. By understanding which product features that potential customers used were most likely to generate a registration, the product team could focus on making those features have the best user experience possible.
Building the Operating Model
To ensure that that the data and insight were embedded in the business, a two-week cadence was established with regular meetings between the Analytics Team and the Product Teams.The Analytics Team would feed in the latest set of insights from their backlog along with their recommendations for action. Those recommendations would feed into the Product Team’s backlog and would be prioritised for action during their sprint planning process. The Product Teams requirements for further insights were fed back into the Analytics Team’s backlog for delivery at a future date.
The outputs from the regular analytics touchpoints were captured and logged with the:
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Insights delivered
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Recommendations made
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Actions taken
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Impact of the actions taken
The Outcomes
From the beginning, I designed for the capability to understand the customer journeys from the product or service into other online channels or into the store. By linking data together from a number of systems and sources in the Insights Platform we were able to identify a user interaction with a service and then subsequent purchases in stores or other online channels sometimes two or three months later.
This became a fundamental component of the tracking of the overall value being developed by the Hub back to the business.
The flexibility and granularity of the Platform also meant that we could not only track the overall strategic value but also focus in on individual features within a product or service and understand whether a user who interacted with that feature was more or less likely to transact at some point in the future. We were in effect measuring feature level ROI within a service. Additionally, with the team and operating model that had been but in place, the client was able to be an insight enabled enterprise.
Feedback
As part of the consulting company's annual 360 review process I received the following feedback about my work on this engagement
Delivery Lead (Stakeholder)
"Neil managed a radical mindset shift across the hub, from leadership to the teams, and made data a prime focus and concern in the hub’s operations. He achieved this with his didactic skills and I was amongst the many whom Neil has trained in the basics of data analytics. Neil could find the appropriate language to address his audience in Show & Tell sessions, leadership presentations and team workshops"
Client Account Lead (Stakeholder)
"Neil clearly has a strong understanding of data and analytics - how to set up teams, how to set up the technology, how to drive insight"
Analyst (Team Member)
"Neil was my line manager and in an informal capacity also my mentor. Under Neil's tutelage I learned a great deal about both analytics and stakeholder management. My recent promotion from analyst to consultant stands testament to the professional growth I have achieved from working under Neil."
Product Owner (Stakeholder)
"Neil has extremely powerful story telling techniques & is able to take complex data, synthesis it, digest & then clearly play back to business stakeholders with the aim of influencing key decision makers."