Algonomy: Empowering Digital-First Strategies for Retail Brands – Create Hyper-Personalised Digital Shopping Experiences (Raj Badarinath, Chief Marketing Officer)
Staying on top of the latest developments in ecommerce is becoming more and more important as the retail landscape continues to evolve. It is crucial for any online business to ensure that they are flexible, agile and ready to adapt to today’s continuously changing circumstances.
Each week, Graham Broughton, co-founder and Managing Director of Storesome (a turnkey marketplace solution and part of the We Are Pentagon Group), interviews thought leaders in the ecommerce industry to shed some light on the latest trends in ecommerce – such as payments, logistics and the newest technologies, to name a few.
In the fourth episode of the new season focused on search and customer experience, Graham was joined by Raj Badarinath, the Chief Marketing Officer of Algonomy. Formed by the merging of Manthan Software and Rich Relevance, Algonomy is a global leader in algorithmic customer engagement. With industry-leading retail expertise, Algonomy connects demand to supply using a real-time customer data platform, enabling 1:1 omnichannel personalisation, customer journey orchestration and customer analytics.
This episode discussed several topics: What is Algonomy and how was it formed? Which types of businesses would get the most out of Algonomy? How does Algonomy reduce the time to market and speed up demand generation? Finally, Raj also shared his insight into some other key considerations, such as privacy laws surrounding the use of customer data – creepy or cool?
This is a summary of a live podcast episode on July 20, 2021. You can listen to the full 28-minute episode here: https://bit.ly/3kKqXZt.
The History of Algonomy
As mentioned, Algonomy was formed by the merger of two companies: RichRelevance and Manthan Software. According to Raj, “the main thesis behind the merger was: how do we end up creating a differentiated value proposition in a very crowded market?” Raj also points out that there are about 8000 companies in various categories, many of which are being formed by the day, so the real question for Algonomy was “how do we help our chosen market, with specific problems, in their particular vertical?”
Initially, RichRelevance came in from a digital perspective; much of their expertise was in web, mobile, the online experience etc. Manthan also approached this question from a digital perspective, but applied more specifically to store enablement. Therefore, they dealt with everything to do with point of sale systems, merchandise analytics, supplier collaboration, and so on. Raj says that they decided to bring these two together, in order to have a fairly verticalised approach from front to back. That’s how Algonomy was born.
Today, Algonomy is the only provider which goes far deeper within the retail value chain than anyone else in the market, bringing together everything from demand signals all the way to supply in one platform – essentially becoming what Raj describes as the “retail operating system”.
If there is anything that 21st-century business has taught us, it is that you have to start with the customer first. Retail in the 20th century was all about increasing the efficiency of the supply chain, but clearly, that is not the world we live in anymore. Nowadays, everything starts with customer demand, so one of the things Algonomy is working on is the concept of hyper-personalisation.
As Raj explains, this whole idea from the front end of demand is being able to say “can I, as a retailer, understand the explicit and implicit signals that my customer is sending?”. By explicit, Raj means what they call zero party data, which is where a customer can come in and say “here’s what I like and here’s what I don’t like”, “I like blue but not brown”, for example. On the other hand, implicit signals are essentially everything a consumer does from a behavioural perspective, such as which styles they tend to frequent.
Algonomy’s engine aggregates this massive data at scale, and unlike what has been seen previously, it ends up creating significantly tailored AI models that take into account your personal preferences, both explicit and implicit.
According to Raj, there are three characteristics that define personalisation:
- The first is personalising for the individual, not just for the segment
- The second is achieving this by using machine learning and AI
- The third is to do so in real-time
So those are the three concepts that define hyper-personalisation from Raj’s perspective. It starts by getting not just an aggregate sense of demand, but an individualised sense of demand that becomes aggregated, meaning “it’s really a bottom-up view rather than top-down, which is where the core difference in my mind comes from.”
To round off the discussion, Graham brought up one of the most recent major issues arising from the latest feats in customer experience: concerns around data and privacy, particularly surrounding the new GDPR laws. Although it is exciting to have shopping experiences which are hyper-personalised and specifically tailored to us as individuals, it can sometimes also be a bit daunting to think of just how much companies know about us, our tastes and our preferences. So, what’s the general consensus?
Well, Algonomy conducts a survey every year which they call ‘creepy vs cool’ – designed to find out what customers think about the collection and use of their data. Raj claims that about 75% of the survey responses say that it is not giving up the data that’s so much of a problem, “it’s the lack of relevance as a consequence that people have a problem with.” This means that although customers are fairly content to inform retailers of what their preferences are, they’re not so happy with still receiving meaningless, non-relevant recommendations after the fact.
To continue, the second case where the ‘creep’ factor increases is if a company uses third-party data to augment what they know about you. In this case, it will seem as if a business knows more about the consumer than the consumer has told them, so Algonomy does not do this. In Raj’s opinion, it would be much better to have an environment where all retailers have to do is take their existing touchpoints and do a better job with it instead of trying to augment with third party data sets. “If I just leverage what I know that you have told us from your prior purchases, behaviour and explicit declarative intent, I can give you a much better experience today given what you’ve told us, rather than trying to infer what we think we know about you”.
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