#data pulse 17
Times have changed significantly since we built the first unified view of customers in Tesco in 2005 working with Clive Humby and the team at dunnhumby. We were cutting edge at the time and had to build all the systems and processes from scratch. Technology has improved with the likes of IBM Infosphere providing MDM systems , and new agile , start-up mentality ways of working changing delivery timetables.
Gone are the days of 12-24 month development programmes, with waterfall methodology and timescales that mean the business imperatives have moved on before they can be addressed.
Hello to technology and ways of thinking about data problems for the 21st Century where leaders can focus on the business problems and organisational process changes required to solve the problems for customers.
The MetLife Wall is a good example in financial services of creating a unified view. It joins all linked customer information in a single place, with one screen that gives all of the information needed to serve customers quickly and effectively. The Wall provides a simple 360 view of each customer across MetLife’s businesses. The interface shows interactions across all touch points (e.g. call centre, in-person interactions with agents, claims, policy updates), connecting more than 70 legacy systems. The application allows customer service agents to reach the information they need with far fewer clicks, and makes it much easier to effectively cross-sell.
The first prototype of the system took just 2 weeks to build. The entire development process, from conception to final product, took just 3 months.
The lessons to learn from Met Life are
- a clear focus on what problem you want to solve with the Unified View of Customers.
- an agile, start-up mentality where you build minimum credible product and then continually improve.
- unified view of customers is a start point to solve business problems not an end point,
data Pulse #27
The Obama presidential campaign 2008/2012 created a step change in how data was used in political campaigning. Obama campaign mobilised a team of experts to give him an unfair advantage against the other candidates. It could be seen as very sophisticated but in reality they were just applying all the simple skills that data-driven Direct Marketers have been using for decades, but doing it with discipline, at scale, and speed.
Obama’s team focused on making sure that Democrats voters would come out to vote on election day, and built a machine that enabled people to be involved and donate their money, time and energy to the cause.
Obama campaign was in “the subscription business”
Obama had clear purpose, but also ran a political campaign in a professional business like manner. he had clear data-driven targets, reviewed daily. Obama built an excellent customer experience that micro-listened so they could target with precision to maximise the impact of communications. They built an engagement ladder to move through in a monitored way from aware, to supporting to donating or actively volunteering.
All messaging was A/B tested to maximise the effectiveness of targeting for different groups. They know that an email from Michelle Obama would appeal more to one group, or from a local Oklahoma name in Oklahoma would work better for a different group.
This enable mass fund raising and mass engagement of grass-roots supporters, and blew away the Republican Party candidates twice.
The rest is history…. until 2016.
data pulse #43
Now I’m not one into female fashion ( just ask my wife) , nor do I hang around the shops but I do love how Tamara Hill-Norton has used data to create a passionate community with Sweaty Betty since she set up the first boutique in Notting Hill in 1998 . Initially targeting “yummy Mummies” but now broadened out to connect fitness and fashion.
Sweaty Betty is a British retailer specialising in active wear for women, featuring in 30 UK stores and 2 new ones in New York and selling significantly digitally. Sweaty Betty aims to ‘inspire women to find empowerment through fitness’.
Sweaty Betty distinguish themselves from the competition by moving beyond traditional retail practices to focus on building an active community. This is achieved through regular Sweaty Betty fitness classes that are actively promoted to its customers. These classes range from yoga, run clubs and boot camps right through to Pilates, and are held in Sweaty Betty stores around the world. For those who can’t attend in person, there are also online fitness classes.
Sweaty Betty was very clear on their purpose and had a very clear story that was developed starting inside the organisation, and building out into their community. A data driven approach to brand building and creating community, loyalty and interaction meant people starting telling the Sweaty Betty story themselves.
Sweaty Betty leverages a broad range of data-driven social tools – Twitter, YouTube, Instagram, Facebook and Pinterest are all used. They also created ‘brand ambassadors’ and allowed customers to have a conversation, helping to underline the sense that Sweaty Betty is a ‘fitness community rather than just a sportswear retailer