As with most businesses today, those in the consumer packaged goods (CPG) industry have, and continue to undergo a rapid transformation in the age of new and emerging technologies. In this typically resource-intensive space, enterprises are increasingly looking to harness the power of technologies such as Artificial Intelligence, Machine Learning and advanced analytics to augment the roles of human teams.
Given the highly fragmented, complex nature of the CPG industry, there are several factors involved in creating an optimal trade promotion strategy. As per a report by the Promotion and Optimization Institute, businesses spend as much as 15-20% of their total revenue annually on trade promotion and related activities. However, traditional trade promotion approaches left much to be desired.
Here, pairing Artificial Intelligence (AI) with a mix of other advanced technological tools has proven to be extremely beneficial, empowering companies with more accurate, in-depth insights based on both present and historical data. According to a report by BCG, CPG companies can achieve over 10% revenue growth using AI and advanced analytics at scale.
If implemented effectively, these technologies can help them unearth crucial, real-time insights to increase their brand’s visibility, enabling them to capture a larger chunk of consumer mindshare, enhance engagement and satisfaction, thereby resulting in more positive sales conversions and retention. To maximize the benefit and secure adequate returns, CPG companies need to weigh various parameters before they implement these advanced tools into their operations, in order to see effective results.
The evolution of trade promotion tools:
Over time, enterprises using manual data-tracking methods made the shift to trade promotion management (TPM) software. This facilitated basic planning and tracking of certain parameters by automating the results. However, it still lacked deeper insight.
An improvement over TPM came with the development of trade promotion optimization (TPO) solutions, which took this a notch above by using analytics to highlight key areas of performance. It enables companies to pinpoint a number of factors affecting trade promotion, by optimizing multiple calendars and dividing it based on factors such as geography, types of products and SKUs.
TPO is still used by a large number of companies around the world. Yet, this still doesn’t fulfil the need for smarter, actionable and more granular insights, and requires significant human intervention and monitoring.
With a larger number of CPG companies looking to optimize their strategies in light of cut-throat competition and rapidly-evolving consumer behaviour, the advent of Artificial Intelligence and advanced technologies led to the creation of Trade Promotion Intelligence (TPI). A completely new category in itself, TPI is centered on AI and allows enterprises to take well-planned decisions by providing real-time insights on a number of business goals.
It is also powered by ML which allows it to function independently and provide smarter trade promotion recommendations with each new bit of data that it processes, be it factors like weather or logistics. With this, companies can identify areas that need improvement, helping them effectively alter strategies to cut costs where necessary and maximize their revenue.
Personalization is the key to fulfilling dynamic consumer purchasing behaviour:
The increasingly digitized nature of sales channels makes it necessary for CPG companies to strike the right balance between both online and offline channels in their trade promotion so that they obtain the desired share of returns from each one.
The approach involves tailoring these to suit evolving consumer behaviour as well as targeted efforts through the relevant channels. With so many options on offer, consumers (millennials, in particular) tend to shop for different products via different channels, depending on convenience, engagement, influences and new trends.
CPG companies need a solution that will help them accurately predict customer needs and preferences and influence their decisions in real-time. This is where Artificial Intelligence and advanced analytics tools step in to help enterprises with accurate recommendations, provided that they have a relevant data pool to work with.
To achieve this, organizations will need a robust data strategy, where extracting what’s relevant from their existing customer data will be extremely beneficial to shaping Artificial Intelligence capabilities specific to their brand. This will help them target the right consumers with the right offer and via the right channel.
For example, Amazon uses AI and analytics tools combined with its large data pools to optimize personalization for its consumers in real-time. We have all seen that once you search for something or viewed product on Amazon, it tends to show up across several other websites, ads, and emails from the company prompting you to have a look at the same product or similar ones.
Once you’ve established interest, the company’s automated engines are working at it to make sure you complete a particular purchase or guide you to a new one. All of this is powered by algorithms that take factors like customer behaviour, buying patterns and search history into consideration. Based on this strategy, Amazon has recorded that 44% of customers make purchases based on the website’s recommendations.
When Artificial Intelligence and data analytics capabilities are effectively implemented at the core of the sales system, enterprises gain access to a goldmine of insights, predictions and recommendations which, in turn, help them personalize consumer experiences. In the long run, starting out with the right data and relevant tools will go a long way in helping businesses to unlock sustained growth with impactful, scalable results.
By Imran Saeed, Director, Analytics at Absolutdata