Marketing as a strategic business partner: mixing theory, research and #data

Marketing is viewed as a key strategic participant in achieving the goals of businesses, both large and small. I thought I’d share how we started to apply marketing theory, practice, and insights from data. There are tons of ‘skate on the surface’ marketing soundbite books, which may sound good at the first glance with no depth. But it’s quite a different thing to work at it so you really know it, practice it, and can share it with other people so it is authentic. It’s about focusing on the real, not the soundbite. Real never goes out of fashion.

Research evidence has shown that consumers interact with advertising in complex ways, especially since we have such short attention spans (Weilbacher, 2003). How do organizations know which way their decisions should land? After all, the Internet can break a business very quickly! So, organizations need a cohesive strategy which aligns all methods of communication to ensure consistency (Porter, 2001).

The overall company vision can be conveyed through the brand, which is shorthand for the company vision. The brand to communicate and distinguish an organization from other organizations. Brands can be viewed as a collective of perceptions that exist in the mind of the consumer, and the process of distinguishing and identifying products is known as branding (Doyle, 1999; cited in Baker, 1999).  Even in the industrial sector, where decisions are made by technical team members, brands can acquire confidence through familiarity (Levitt and Levitt, 1986).

Building a brand involves four key concepts: quality, service, innovation and differentiation (Doyle, 1999). Brands can also migrate from mature technologies to encompass new technology (Doyle, 1989). For example, over at Data Relish, we mix mature Business Intelligence technologies with new and upcoming technology, Artificial Intelligence is enjoying rapid growth (Xiong, 2019).

How can you tell what’s working for your organization? Particularly in the current economic climate and Brexit-dominated climate, it is important to identify the most targeted opportunities to maximise growth opportunities. It’s also important to collect customer feedback and references, which can be incredibly enlightening and occasionally even heartwarming! So what’s the secret?

  • The secret is the data. For example, at Data Relish, we have conducted some analyses in order to look at targeting and positioning better. It’s like having an arrow, and the segmentation is equivalent to pulling the arrow back so it gives you more power to go further.
  • It’s great to be insight-driven, but it’s also good to be data-driven and evaluate what the data actually says. It’s a balance.
  • Also, have good tools.

To do this, we used Power BI to examine the data from source systems such as HubSpot, FreeAgent and Insightly to understand better what was working from the marketing perspective. We also send the results to an independent marketing consultant for the purposes of replication and verification.

Obviously I’m not sharing the findings here, but it is safe to say that the results were enlightening. The UK Government estimates that AI could add an additional USD $814 billion (£630bn) to the UK economy by 2035, increasing the annual growth rate of GVA from 2.5 to 3.9% (UK Government, 2017). By asserting experience as well as expertise, one thing that was working was that we were viewed as ‘trusted advisors’ in AI, helping to lead organizations at success by working with the C-suite to make the data work, and work hard to add business value. It became clear that we were perceived as a proven safe trusted advisor with experience in what’s perceived as a ‘young’ field, and this is a key differentiator which is proving invaluable in gaining clients in a variety of sectors. It seems that everyone is an expert in AI these days! But we could actually show it.

So, we made efforts to have real chops and apply our research and findings to our own organization, and to really analyze the data, and to apply marketing theory and practice to improving , informed by the academic literature. Data can help us to understand the research to explore and elicit reasons for avoidance of brands (Lee et al., 2009).

I’ll blog more on the journey with marketing with Power BI, HubSpot, FreeAgent and so on, but the main takeaway here is that you always learn something by having a fresh look at the data. Additionally, it’s important to get the results independently verified. If you need a hand with these concepts, don’t hesitate to get in touch over at Data Relish.

References

Doyle, P. 1999, ‘Branding’ in Baker, M (ed.) The Marketing Book.; 4th ed, Chartered Institute of Marketing., Oxford, UK.

Doyle, P. 1989, Building successful brands: The strategic options. Journal of Marketing Management, vol. 5, no. 1, pp. 77-95.

Levitt, T. & Levitt, I.M. 1986, Marketing Imagination. Simon and Schuster.

Porter, M.E. 2001, Strategy and the Internet. Harvard business Review, (2001): 63-78.

Porter, M.E., 2008. The five competitive forces that shape strategy. Harvard business Review, 86(1), pp.25-40.

Weilbacher, W.M. 2003, “How Advertising Affects Consumers”, Journal of Advertising Research, vol. 43, no. 2, pp. 230-234.

UK Government 2017,  Executive Summary. Available: https://www.gov.uk/government/publications/growing-the-artificial-intelligence-industry-in-the-uk/executive-summary

Xiong, X. 2019, “Analysis of the Status Quo of Artificial Intelligence and Its Countermeasures”, 2018 International Workshop on Education Reform and Social Sciences (ERSS 2018)Atlantis Press .

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