Online shoppers come into contact with so many data points throughout their e-commerce journey, it can be a daunting task for CG and retail brands to make sense of that data. Add a global pandemic into the mix – complete with social distancing – and the e-commerce boom that has ensued has made data even more plentiful and robust.
Some organizations might see this as a challenge, where others can use it as an opportunity to better understand their consumers. That’s why CGT caught up with Udayan Bose, founder and CEO of NetElixir, to talk about some of the challenges to successfully capture this newfound data, and more importantly, what they can do about it.
CGT: What are the biggest challenges in capturing and using e-commerce data?
Bose: There are three key challenges in capturing and using e-commerce data:
- Data accuracy.
- Piecing together different pieces of the puzzle to gain a deeper understanding of search - shop - buy behavior.
- Creating a data framework that allows for greater agility at scale that enables businesses to gain market share in a rapidly changing business environment.
- Last but not least, the impending challenge of 3-P Cookie deprecation has the potential to disrupt the entire digital marketing industry. Businesses need to have a plan in place to address this mega shift by investing in 1-P / 2-P technology infrastructure.
CGT: What are the "four Ps" in marketing and why do marketers need to understand them?
Bose: Product, price, promotions, and place (or, channels) comprise the 4 P's of marketing. A successful marketing plan needs to take into consideration not just each of these elements individually, but also how they affect each other.
Today's rapidly changing digital marketing world has made it an imperative for the marketing plan to be dynamic. Marketers need to continually measure the plan's performance and strive towards achieving a state of optimality in their 4-P mix.
CGT: How can e-commerce data boost the top and bottom lines?
Bose: By carefully analyzing the shopping funnel and consumer-segment shopper behaviors, marketers can create an optimal marketing mix that boosts both top and bottom lines. Given the rapidly changing nature of the e-commerce industry, this marketing mix will need to be dynamic and relevant to the evolving shopper behavior.
CGT: What impact did 2020 have on e-commerce data? In turn, what do you expect in 2021?
The pandemic led to an exponential increase in e-commerce sales. This e-commerce surge was a result of greater penetration (more people shopping online) and an increase in usage (shoppers buying more online).
Per eMarketer, in the United States, 7.4 million people engaged in online shopping for the first time ever. The shopping pattern of these shoppers was different from experienced online shoppers.
Marketers had to engage in heavy consumer analytics to identify and segment the various shopper personas and create marketing initiatives that were more targeted (personalized) and relevant. The e-commerce explosion also led to an exponential increase in the number of data points pertaining to the e-commerce funnel.
In 2021, as the number of consumer touchpoints increases and as e-commerce moves to a 1-P Cookie world, marketers need to invest in technology capabilities that help them make this transition. The contribution of e-commerce sales to total retail sales increased from 15% in February 2020 to 22% by the end of this year. We believe this percentage will increase further from 23% to 24% by end of this year. As brands intensify their focus on shoring up their DTC efforts and taking control of their sales channel, we will see interesting innovations in the e-commerce space.
CGT: How are AI and ML changing the approach to data analytics?
Bose: The advances in AI and ML are helping marketers better analyze, understand and derive insights from the various data sets. The predictive ability of AI/ML is enabling marketers to plan and forecast better.
For example, NetElixir's Customer Intelligence Platform, LXRInsights, algorithmically segments shoppers into different value segments, uses AI to predict the repeat purchase propensity of first-time shoppers, and estimates the life-time value for new shoppers, and also, proactively identifies shoppers that are more likely to churn. AI truly is transforming data analytics by making complex predictions easy at scale.