Serviceplan Group Germany
Fri, 13 Sep 2019 11:08:33 GMT
In order to master the challenges posed by the advancing digital transformation, many advertisers, media companies and agencies from the marketing and communications sector have invested in data-based technologies and infrastructure heavily in recent years. Despite this investment, however, they are often unsuccessful in getting this 'horsepower' on the road and turning it into a relevant competitive advantage – this is due, among other reasons, to HiPPOs (Highest Paid Person’s Opinion) frequently operating on instinct.
The truth is that the digital transformation that companies are facing is not just a technical one, but also - and primarily - a cultural one. For that reason, according to a Capgemini survey, problems in corporate culture are hindering the digital transformation to a far greater extent than factors like outdated IT systems or resource constraints.
At Plan.Net NEO we are intensively focused on establishing a consistently data-driven range of services, and on the data-based integration of media and content. These data, and the insights gained from them, are intended to serve both the creatives and the media experts on our team as 'fuel' for the development of relevant content and its effective media activation.
This far-reaching process of transformation has taught us a great deal – not least about the critical importance of corporate cultural aspects on the journey to becoming a data-focused agency. The key lessons that we’ve learned can be distilled into five practical tips that are valid not just for agencies, but for all organisations.
1. Develop and promote data skills
A data culture that is lived in practice begins with 'empowerment' and the democratisation of data. It pays to equip your team with a range of technology to enable them to analyse data and make data-based decisions for themselves.
This is because the problem often faced by companies isn’t a lack of data, but a lack in the process of getting from data to decisions. When a company’s datasets are concentrated in the hands of a small number of experts, the potential that they represent for the company’s commercial success remains largely unexploited. In order for the company to extract the greatest possible benefit from them instead, every employee needs to be able to access the data and integrate them into their own decisions on a daily basis.
This doesn’t mean that every employee needs to become a data scientist (or that this should be possible). It does however mean that everybody should either already possess at least basic analytical skills, or be able to acquire them through qualifications, in order to be able to assess data-based issues and use data in decision-making.
2. Technology has to adapt to the people who apply it, and not the other way around
The technology and tools used for realising the everyday use of data in the workplace play an important role. If these are to be used by team members other than data specialists, the following characteristics in particular are key to ensuring that they are broadly accepted among users and perceived as being usable and valuable in practice:
• Convenience and intuitive operation: The easier and more intuitive data handling is set up for the user, the sooner they will appreciate its value for their daily working quality and no longer want to miss it.
• Intelligent data visualisation: Dashboards and other display formats make it easier to rapidly grasp the central messages of the data and ensure that everybody involved sees the same picture. Reducing complexity in this way is an important prerequisite for efficient individual work and effective teamwork.
• Opportunities for dynamic interaction with data: Decision-making processes seldom follow a linear path. That’s why data analysis platforms should make it as easy as possible to explore data, test hypotheses, and try out different scenarios.
A good example of data 'empowerment' enabled through technology is the 'Brand Investor' tool developed at our sister agency Plan.Net Business Intelligence, which we have been using for some time.
The tool enables our consultants and planners to determine an optimally efficient communication plan for our clients’ brands, without needing to rely extensively on the skills of data and BI specialists for this kind of demanding task as they did before. The brand investor uses artificial intelligence to make the knowledge gained from over 2,000 calculated media plans and more than 200 conducted marketing mix models available at the touch of a button. With the help of an interactive, visualisation-oriented user interface, users operating in 'do-it-yourself mode' can optimise the budget allocation for their data-based decision-making. The results are shown through the significantly shorter time for planning scenarios of all kinds than before, and through forecasting their impact.
3. Encouraging critical curiosity in interactions with data
Being fundamentally curious is a key driver of innovation and growth, and also lays an important foundation for an analysis culture. After all, this is all about seeking out new approaches and the desire to try things out and experiment. At the same time, and especially where data are concerned, the cultural framework also needs to ensure that critical thinking doesn’t get neglected.
In particular, a 'data first' corporate culture mustn’t mean blindly following indicators based on bare figures without discerning the broader context. It’s far more important instead for the culture to motivate team members to be able to interpret data critically, so that your organisation doesn’t only base its decisions on reliable data, but also knows when it’s better not to do so with regard to data quality.
4. Set the example for practising a data culture from above
It may be less surprising to hear that successfully establishing a data-oriented corporate culture starts at the top. It’s important for management not to lose sight of this, however, as real life unfortunately shows that this is all too often forgotten amidst hectic everyday schedules.
Management teams need to set out a clear vision if data are to become ingrained in a company’s DNA. Rather than simply developing and diligently communicating this as a concept, however, it’s also important to set an example by practicing it on a daily basis. The key buzzphrase here is 'walk the talk': follow up your own words with actions. When management teams set a good example, other managers and employees will be sure to quickly adapt their own behaviour – whether consciously or unconsciously.
At the same time, cultural change is also a two-way process: it works both from the top down and from the bottom up. It’s the role of management teams to set an example, but also to give focused encouragement to suitable and engaged employees, who will then go on to function as multipliers, or 'agents of change', in their respective teams.
5. Don’t go with your gut
Closely linked with the previous point, our recommendation is to make every effort to replace gut feeling consistently, permanently and throughout the company with decisions that instead are based on data and facts.
Many company teams are still led by HiPPOs. This means that decisions are often made by the highest-paid members of the team, who can often be far removed from the actual issue or problem. This is especially problematic in cases where these HiPPOs make decisions based on their instincts, as informed by their subjective experience and intuition, rather than reaching out for their teams to provide analyses and recommendations supported by data which would enable them to arrive at a decision objectively.
If data plays a genuinely central role for corporate culture, then as a requirement, all decisions taken in the course of daily business life has to be based on those data – without exception.
Thorsten Stork is managing director Plan.Net NEOview more - The InfluencersServiceplan Group Germany, Fri, 13 Sep 2019 11:08:33 GMT