Driving improved customer and business outcomes through automating Standard Life’s BI capabilities and focusing on KPI interpretation
Standard Life were confronted with a number of challenges that they were keen to tackle with our help. Their existing customer programme dashboard reporting was comprehensive and met a high standard, but it took too long to refresh – it was a manual weekend task. This meant that strategic business leads (product owners) did not have access to real time insights and were waiting a long time for questions to be answered. This slowed down decision making, despite there being an aspiration within customer-focused activities to test, learn, and scale. This ambition was impossible to enact given that the underlying feedback loop was not timely.
The current skilled senior analysts were pulled in multiple directions. Whilst they positively impacted a wide range of aspects of their customer activities, they didn’t have the luxury of time to review, refine, and automate their solutions. Too much analyst time was being spent on data engineering and production – not on interpreting results and understanding what they meant for customer outcomes. Additionally, there was a lack of consistency in KPI definitions, calculations, time frame, and data sources.
Overall, there was a keen desire within the business to move towards greater insight creation and usage of data science techniques as led by a new chief data scientist. Our challenge was to help Standard Life put the foundational, consistent building blocks in place first. This would then provide their experienced analysts time to up-skill and pivot to higher-value analytics.
Our approach began with working with the business leads of each team to capture objectives in order to understand the actionable insight the squads require so they can make decisions. This helps us to understand what activities are driving value and so should be scaled/rolled-out. We worked alongside the client’s senior analysts to review what data existed already and to understand potential KPI and data gaps. We then engaged the business to agree definitions, data sources, and calculations.
This work allowed us to establish the right KPIs and control group methodology to enable effective reporting. We concluded with creating a connected view of measurement within Tableau dashboards. This allowed self-serve by analysts, squad members, and business stakeholders to drill down across various levels of detail covering channel engagement, completion of customer call-to-action, and measurement of commercial impact.