Traditional Data analytics and BI systems: a slow-track to nowhere?
Why the only CPG companies who are thriving are those quickly adopting AI?
Marketing analytics is used in less than 40% of marketing projects. More than 60 percent of Fortune 1000 Chief Marketing Officers cannot quantify the impact of marketing (see our previous article).
The measurement and assessment of marketing’s impact (the real return on investment) of each investment lever and of the overall commercial spending, is still an open challenge. There are good reasons for this - traditional analytics and insights have reached obvious limits.
The input (data) is not the issue
On the data side, concerns are limited:
Data and datasets are in an ever increasing number, and widely available.
They are often relevant, in quality and diversity, covering most causal factors (sales, consumer, satisfaction, brand, competition, economics, weather, financial, etc.).
The issue lies elsewhere:
These relevant data are often disconnected, semi-structured or unstructured.
They are being, at best, only partially exploited and not comprehensively analysed.
The dark side of the outputs
Whether they look at specific areas (e.g. price and promotions), or at broader business growth and marketing RoI, decision-makers in sales, marketing, and supply-planning rightly complain about a combination of issues that prevent them to deliver better performances. Despite a range of analytical tools and BI solutions at their disposal (which have been the best possible tools for a number of years), they are facing their limitations:
Insights are backward-looking: it tells you what happened, sometimes (to a limited extent only) why it happened. In short, companies keep making decisions based on what happened, not what is likely to happen.
Extracting value from the data: single sources data are being used; only a few causal factors (and more often than not, just one) rather than all causal factors are considered to draw conclusions. It doesn't deliver accurate insights and a comprehensive understanding, when commercial decisions are highly complex and must be optimised across the board. Sales and marketing decisions impact each other, they work well in synergy, but can also nullify each other.
As a consequence, insights only provide limited accuracy, to say the least, and very limited granularity (when decisions must now be made at the most local, audience specific level).
Teams have to make decisions based on a number of fragmented analysis and reports, which never offer a complete overview, or the option to really drill down.
The last nightmare is about lost time: the time it takes to analyse data is about days, weeks, sometimes months. Many companies get quarterly reports on media campaigns, a few months after the end of a campaign. As data become quickly obsolete, and learnings from a campaign that started 6 months ago is losing relevance, marketers are facing huge latency in their decisions. They certainly can’t make agile decisions (including in-flight optimisation). The business reality, though, is that more decisions have to be made faster, in what seems a shrinking time frame. This proves to be costly for companies:
Data have a direct cost: companies spend a part of their budget building or acquiring data sets; they also incur the many costs of analysis (systems, processes, resources). The more an organisation is data focused, the more it is reliant on increasing resources (internal data analysts, external agencies).
Crucially, under-used data lead to a huge opportunity cost: companies’ outcomes are underperforming because their commercial and supply decisions are far from optimal. It impacts their costs, their sales level, their revenue and profit performance, their RoI, and of course their growth.
Teams in the most performing organisations are looking at ways to solve their insights and decisions challenges, that are based on disjointed systems, models, spreadsheets (and, of course, pen and paper!). This is where Artificial Intelligence is a game-changer.
Learn more: how AI can transform RoI and create unique advantages from data, what are the best practices of adoption for maximum impact.
Attend the event Singular Intelligence and Microsoft are organising (in London UK, on 16 November 2018), about:
“Transforming RoI and agility in marketing and supply-planning decisions through AI”.
You just need to register here fo free: https://www.eventbrite.co.uk/e/transform-roi-agility-in-marketing-supply-planning-decisions-through-ai-registration-49336987257
_______________________________________________________________Jean Littolff (ex Nielsen and GfK) is Chief Revenue Officer at Singular Intelligence; Sarvesh Kumar is Founder and CEO of Singular Intelligence; Steve Gladwell (ex Pladis / United Biscuits) is the Industry Director at Singular Intelligence. www.singularintelligence.com