1. Meaningful measurement
Throughout the pandemic there has been a need to understand, quickly and accurately, what is happening at that point across the whole organization, and how this is changing over time. But the data collected must add real value in its ability to inform business strategies.
Shifts in consumer behavior, both in terms of what is bought and how (in bulk, or in smaller samples, for example, as well as which channels are used), are essential to understand; this is the ultimate demand driver. Sensing this demand and eventually meeting it requires analyzing it against inventory available.
Simple KPIs such as stock levels and flows can smooth the manufacturing process; although this looks straightforward on paper, it requires an industrialized pipeline bringing together a wide range of data.
Mental health has also been an ongoing theme during the crisis, and knowing about employee happiness is key – from the early days of working from home, through potential burnout as work-life balances blurred, to the gradual return to the office.
However, the rapidly shifting sands of the Covid-19 world makes it important to avoid using this data to draw immediate conclusions about permanent shifts in behavior, nor should it be used to make long-term decisions. For example, while there’s no doubt the requirement for office space is different from its pre-pandemic levels, it now seems unlikely that city centers and business districts will become the ghost towns predicted in the initial fallout.
2. Effective e-commerce
No surprises here – the accelerated move to online retail could be viewed as one of the saviors of the pandemic. Organizations around the world rapidly shifted the way they did business to meet the changed circumstances in which they – and their customers – found themselves.
But although there is no doubt that e-commerce (or connected commerce) is the way forward, a full digital readiness assessment provides a snapshot of the retailer’s current capabilities, and ensures that they can scale.
Direct access to consumers is also valuable, as their changing online behaviors and purchasing patterns can inform strategies for media spend by product and keywords on which to advertise (some of which might be unexpected). Co-branded campaigns enable brands and retailers to benefit from shared data, amalgamated audiences and combined budgets, with both Facebook and Google offering this capability.
Marketplace strategies should also be assessed so the right products are being promoted in the most relevant way on the marketplace that is most suitable.
Further reading: 14 global consumer behaviour trends for 2021 and beyond.
3. Marketing budget protection
Marketing budgets are vulnerable during challenging trading periods, and although cutting these costs is usually proven to be short-sighted, protecting them calls for smart arbitrage and campaigns that deliver ROI.
This requires focusing on the right KPIs – with these differing between industries. Demand for products and services that were available and relevant during the pandemic (such as digital entertainment and online retail) increased; however, the subsequent rise in digital media spend in these sectors drove up advertising costs and damaged ROI.
Selecting the campaigns, segments or investments that make most sense from a Customer Lifetime Value perspective versus short-term / one-off returns for instance is needed to limit inflation.
Other areas however (automotive and travel, for example) witnessed demand for their offerings slow down or stop altogether, resulting in less media spend. For these companies, temporarily moving away from traditional performance measures to ‘micro-conversions’, such as leads generated, newsletter sign-ups, was – and maybe still is – a relevant strategy. Although these are not sales, they provide a scale of consumer interest and intent for when the market rebalances.
And even during ‘slow’ sales periods, the value of branding campaigns should not be underestimated. Reduced demand for products sees a subsequent reduction in demand for advertising, which lowers media prices, making communications low cost, with the added bonus that competitors have often stopped advertising.
As well as building market share, this helps to keep the brand front-of-mind with consumers (think ‘Thank you for not riding with Uber’), while engaging content is a good route to capture all-important first-party data.
4. Data-driven supply chains
As referenced above, understanding demand and having stock visibility is key; it reduces the inventory that needs to be held, as well as lead times.
A supply data model is a core element of the end-to-end data-driven supply chain that underpins this insight. Data from enterprise systems such as SAP, media data, digital data, sell-in data (sales from the manufacturer to distributor) and sell-out data (sales from the distributor / retailer to the end-customer) needs to form a fully-automated pipeline, with all relevant parties having access to stock level and flow information.
Demand sensing projects add further insight, allowing companies to use short-term trends as they happen, in order to better predict consumer demand by product, region, market, time of day, etc.
Further reading: Digital maturity of supply chains still in its infancy.
5. Artificial intelligence
AI is no longer ‘new’. Indeed, according to ‘The state of AI in 2020’ by McKinsey & Company, 50% of respondents reported that their companies had adopted AI in at least one business function. Its adoption is driven by its ability to help companies reduce costs.
However, AI is not a silver bullet; to realize its full potential and maximize returns, business leaders need to shape the structure of their organizations to facilitate its workings.
An effective AI system cannot be overlaid across the whole enterprise; rather it needs to be organized by business function, with programs built by small, interdisciplinary teams that include a product owner, a data scientist, a data engineer and a machine learning engineer.
These programs are powered by algorithms, which are weakened without a strong data pipeline input and clearly defined outputs. And although AI is built on technology, successful implementations also require time and energy to be focused on embedding its principles in the organizational culture.
Further reading: Five best practices for driving value from artificial intelligence.
6. Data management
Today’s businesses have data at the center of almost every function – indeed it underpins each of the recommendations made above. This status makes a strong case for digital leaders putting structures in place to ensure the organization’s IT assets enable effective data collection, storage, processing and outputs.
Data governance programs of this nature have been shown to reduce the time it takes to launch a data project, as well as improve the accuracy of decision-making and analysis. With such governance in place, all data projects are accelerated and organizations set solid foundations for the future activities.
7. Digital culture
The pandemic has promoted the need for a strong digital culture – and in highlighting the long-term possibilities to business leaders it has aligned them with the goals of their digital counterparts.
This offers opportunities to reinforce and accelerate digital transformation plans (or put them in place if they are not yet formalized); their value is clearer in an environment in which change has proved essential for the survival of the enterprise. Within that, digital leaders can launch extensive up-skilling plans to ensure everyone has the knowledge they need for a future-facing digital and data-driven organization.
The technology is there – it’s up to the modern, post-pandemic organization to integrate it into its culture.