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 #17
It’s tempting to think of customer data as the new oil.
Combined with advanced analytics, it offers the promise of marketing nirvana. By perfectly profiling an individual customer, marketing can be truly personalized, improving a customer’s experience, and eliminating waste.
But customer data isn’t a natural resource. It’s generated by people. And as our connectivity increases, so does our awareness of the data being collected and the erosion of our privacy.
With customers increasingly seeking more control over the data they share and with whom, access to customer data will become increasingly valuable, a source of competitive advantage, and a privilege to be earned. Brands will need to demonstrate to customers that they can be trusted with their data.
There are a number of practical steps that should be taken now:
- Make sure you are using the data you already have to improve the customer experience, so it’s clear to customers what value they are receiving in return. This may seem obvious, yet I’m still struck by how infrequently the data I’ve shared is used to improve my experience. My inbox, for example, is still full of mass rather than personalized emails. Why not let customers feel the benefit of their data?
- Sainsbury’s email programme highlights which of their promotions and which manufacturer coupons a customer might be interested in, based on their purchase history.
- Coop emails are linked to promotions in your favourite store on things we think you would like to buy based on previous shopping.
- Starbucks use location data to prompt offers on the phone when you are near a starbucks
- Only collect the data that’s essential to deliver the benefit to customers, rather than everything you can. And be clear about what data you need to collect, the reason why you need it, and what benefit they will get in return.
While data security is certainly a complex technical and legal challenge, it’s underpinned by a question of brand mind set.
If customer data is viewed internally as a commodity, then it’s something to be extracted from customers and traded…and customers will be wary, as behaviours will give the brand away.
But if access to customer data is viewed internally as a privilege, where we don’t own a customers data it’s their data we are only curating it and looking after it to improve our customers experience then it’s something precious that has to be protected…and the resulting behaviours will inspire more trust among customers.
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
data pulse # 33
We should not be frightened to use data: People have been recording data and creating information for thousands of years.
Data use is older than the written word and has been used through history to provide information:
75,000BC the Blombos Ocher Plaque is thought to be the first recorded piece of data.
In 850AD Al-Kindi examined the frequency of letters in text to systematically create and crack coded messages.
In 1662 John Gaunt analysed mortality figure (in an early excel spreadsheet) as a means to predict the onset and spread of bubonic plague.
In 1855 Florence Nightingale used advanced visualisation techniques to make her data more persuasive for the generals and convince them that more soldiers were dying in the hospitals of the Crimea than on the battlefield, and so allow her to use her lamp.
In the 21st Century there is more data and even more being created every day but the same simple principles apply.
Be clear on your outcome and then decide what data you need to tell the story you want to tell.
Anyone can use data, not just the analysts and data scientists. You just need to give them the confidence , skills and tools to do so.