September 2014 - Page 3 of 3 - Global Marketing Alliance

Brand followers – amounts are irrelevant to marketers, report shows

Connections don’t equate to brand engagement.

More than half of Britain’s adult population engaged in social media do little to increase brand interest and create positive associations with brands online, finds Kantar Media in a new social media segmentation from its TGI Clickstream study of online consumer behaviour.

Richard Keogh, head of Kantar Media TGI UK, said: “This new segmentation provides crucial insights into the level of engagement and influence that social media users have online. The different segments show that clicks and connections alone will not reveal consumers’ actual engagement levels.”

Based on an analysis of the social media connections and engagement of more than 50 million adults (aged 15+), TGI’s segmentation uncovered six groups of social media users:
1. Social Spectators – a disengaged group with a respectable number of connections, but the least likely of all social media users to buy goods online, or to read or update their social media accounts. Neither do they post product reviews or follow brands online. They tend to be older and, because they don’t carry high economic or cultural capital, are unlikely to have much clout or spending power for brands.
2. Online Experimenters – are potentially very valuable for brands to target given their crucial combination of purchase power and online engagement. Accounting for just 10% of the adult social media-using population, this group are more likely to be older and particularly likely to engage with brands and to buy products online.
3 Connected Engagers – have the highest level of connections and influence. They account for just 3% of all social media users, but, because they lack economic and cultural clout, may not be the big spenders. They are, however, most likely to spread the corporate word online.
4. Connected Dabblers – this group represents 10% of the social media-using population and has a high level of connections. They follow brands on social media but are less likely to post reviews about products/brands. They are engaged but less influential than Connected Engagers.
5. Passive Socialites – have a high level of connections but don’t follow brands or post reviews. This means they have a low level of influence. They account for 4% of the population.
6. Credible Contributors – account for 22% of the population. This group has an average level of connections and engagement, being highly likely to follow brands and post reviews online. They are active and engaged with medium amounts of influence.

Keogh added: “Marketers should review who they are targeting online to ensure they are directing their social media activities (and marketing budget) at the most appropriate audience.

“For instance, Social Spectators display very little online engagement in spite of having a moderate number of connections. Connected Engagers however, who are most active online have minimal economic or cultural clout. Comparatively, our Online Experimenters, who have relatively few connections online, could prove particularly influential in driving sales and growth for brands. What they lack in connections they make up for in the way they embrace social media and online purchasing.

“Marketers need to look beyond widely accepted metrics to specific evidence of engaged online activity to determine how valuable consumers are. Now is a good time for brands to review who they’re really speaking to online.”

The financial value of data quality

Boris Huard says the valuable role of the chief data officer is dawning on board members.

We are constantly hearing about the fact that the issue of data quality is rapidly moving up the corporate agenda to become a board level discussion – dawning the age of the chief data officer.

One of the key drivers for this is the realisation that data has a financial value – either in its own right or via the impact it can have on business processes and outcomes that drive the profitability of the organisation at large.

However, despite this, there is still all to often a sense of apathy towards tackling the data quality challenge. As a result, many organisations are still struggling to make the case for larger corporate wide data improvement initiatives. This is largely driven as a result of ‘data quality champions’ within the organisation being poorly equipped to make the linkages required between data inaccuracy and overall business performance.

When it comes to data quality, it’s essential to start thinking about the long game and how it specifically pertains to customer or party data. Customer data is not only the lifeblood to the effective operation of an organisation – it also has commercial value. This will become more and more apparent as business models around data evolve. Gartner states that by 2016, 30% of businesses will have begun directly or indirectly monetising their information assets via bartering or selling them outright.

So why do organisations struggle to put a value on their customer data assets?

One of the key factors here is visibility and ownership at a corporate-wide level. Many organisations today hold data within a multitude of silos perceived to be owned by a range of individuals around either lines of business or the IT department itself. This is probably exemplified best when you look at statistics around the roll out of data quality technology investment, with most deployments pertaining to one project or department and very few spanning more than three projects or departments.

Another key challenge is that there are often ‘hard’ and ‘soft’ benefits associated with any technology investment. Many of the benefits of investing in data quality are perceived to sit in the ‘soft’ (difficult to prove) bucket. This is because a lot of the upside sits in improved operational efficiency. Take labour productivity as an example. Gartner states that data quality impacts overall labour productivity by as much as 20%. This highlights the importance of data quality as a critical enabler to process quality. Data champions within organisations today need to start mapping the impact of data quality back to real life – and ideally measurable – business processes such as customer care performance or on time delivery.

One size fits all never fits anyone particularly well . . .

Therefore, to convince any board to move forward with an investment in a data quality initiative, it’s essential for them to see for themselves the cost of data inaccuracy as it pertains to their own organisation. The good news is technology can enable this utopia and the market at large is starting to wake up to that fact. This is evidenced in the circa ten per cent increase in the adoption of data profiling and discovery tools between 2012 and 2013.

The key is to select technology that can tell you not just the percentage of data inaccuracy that exists in your organisation’s customer data today, but to connect the dots between inaccurate customer data and ‘things’ such as customer value, helping to put a value on your data quality problem. Taking this approach in the early stages of scoping a data quality initiative will give you the ammunition you need at board level, while identifying the low hanging fruit for data improvement.

Boris Huard is MD, Experian Data Quality.