Unlocking the power of AI for retail
Karen K Burns, CEO & Co-Founder of Fyma, looks at the broader implications of introducing AI into the retail sector, where the pushback from decision-makers is coming from, and what the future of the industry looks like.
The retail industry has undergone a rapid transformation over the last 18 months, and the pandemic has raised a plethora of questions about the future of the industry. Although not all these questions can be answered with certainty, we do know that the role of technology in retail has dramatically increased, and we aren’t expecting this trend to change.
‘AI’ has become something of a buzzword in retail, with retailers becoming particularly interested in how artificial intelligence (AI) can be utilised to drive productivity and cost savings. However, there has been significant confusion on the implementation side.
In this article, we’ll start to understand why there is an ‘AI Knowledge Gap’ between retail decision-makers and their teams, and the hindering effect this has on innovation. We’ll also look at how AI can actually be applied to garner sophisticated insights and analytics that allow marketing teams to tailor services and reduce overhead costs.
The retail landscape is changing (and indeed, already has) in a number of critical ways, and it’s important to recognise this if we are to break down the existing stigmatisation of AI and get the best out of it. The revolutionary qualities of AI derive from our freedom to get creative with how we use it, so thinking outside the box is essential. This, matched with increasing awareness around AI’s limitations and the pre-emptive digitalisation efforts required to use it effectively will catalyse innovation in the sector.
Understanding your customer
When it comes to the AI Knowledge Gap in retail, the biggest driver of conflict is between what some retailers think they know about their customers (i.e. what decision-makers say), and what other retailers hope to know about their customers (i.e what innovators say).
What’s key to understand here is that many retailers have been in the field for a long time, and they combine a certain amount of ‘gut instinct’ with legacy insight tools to formulate an image of their customers’ behaviours. However, this isn’t an all-that-reliable method for running a business. AI functions on real-time data, and is far more reliable when it comes to reporting behavioural trends.
Those that fail to recognise the power of AI are also failing to understand the three broader drivers behind changing customer behaviours:
1. Generational change
It’s the first time in history that Gen Z shoppers have made up the 16-21 demographic. This is a group of shoppers that have never known life without the internet. They’re exposed to more targeted ads and a larger number of brands (both independent and chains) on account of the internet and social media. They’re also more driven by sustainability than their parents and have grown up as online shopping has boomed, so enticing them in-store is a major challenge.
2. The rise of contactless shopping
Periscope’s recent report by McKinsey, which looks at the shift in shopper behavior and expectations before and during COVID-19, demonstrates how safety and convenience are now critical for the in-store experience, and a ‘frictionless experience’ is a top priority. In fact, consumers being able to quickly and easily find what they’re looking for jumped as much as 14 percentage points from March to June. What customers want out of in-person shopping looks dramatically different than it did two years ago.
3. Rising expectations
e-commerce has evolved so that consumers now expect more personalisation and convenience when shopping. This manifests itself in a number of ways, notably, shoppers are savvier with getting the best deals, and in-store shopping trends are skewing towards more click-and-collect orders.
Realising what AI can really do for you
Given these changes to consumer behaviour, retailers are having to think creatively about how to approach their marketing strategies. As it stands, legacy tech such as footfall counters are commonplace in urban spaces. Once upon a time, this tech was a game-changer, and the value of insights we could gain from it was much higher.
However, advanced AI algorithms can do much more than count footfall. They can examine consumer demographics, social media impressions and digital footprints, identify what direction a shopper is travelling, how the weather and time of the year affect all the previous, and much more all at once.
So, why are decision-makers reluctant to bring the tech onboard?
There are two layers to the challenge of introducing widespread use of revolutionary AI in retail:
- Unsticking retailers’ loyalty to legacy systems: ‘This is the way we’ve always done things and it’s worked for us so far – plus, we’re locked into our contracts for another two years…’
- Educating decision makers on what AI can (and cannot) do: ‘We’ve just paid an arm and a leg for AI, but we don’t understand the data!’
Vendor lock-in, loyalty to legacy tech, and a lack of skills to work with AI are serious inhibitors of innovation in retail. Fear of change only slows down progress, and we will see those retailers that can’t keep up drop out of the market, while those who can invite big-data analysts to join their ranks.
Likewise, it’s not helpful for businesses to put all their eggs in one basket when it comes to AI; there are no limits to the use cases for artificial intelligence in retail (check out some examples here), but AI won’t solve all your problems at once. It’s all well and good paying for access to deep, actionable data insights in real-time (since doing so can help you improve customer experience, boost revenue, and make better planning decisions), but if your people aren’t educated in how to use and tailor the algorithms, then the return on investment will be low.
Compliance, compliance, compliance
It would be remiss not to recognise that the revolutionary qualities of artificial intelligence are held in the balance by regulation and compliance. It’s essential that algorithms are sophisticated, unbiased and that data is stored and used correctly.
The good news is that we can construct AI with legal frameworks in mind, ensuring compliance with guidelines such as those outlined in the GDPR and CCPA is inbuilt from the ground up.
Protecting citizen privacy has become a must, with social media giving consumers the power to catapult the [un]ethical pulse of any company into the limelight at any given moment and cause serious reputational damage. What retailers need to recognise is that AI does not need to collect or analyse personal, biometric data (such as race or heritage data) to be effective. To ensure you store all the data correctly and comply when using it, you should just avoid gathering personally identifiable data without consent – i.e. peoples faces, altogether.
Consumer attitudes, particularly in Europe, are vehemently against the use of facial recognition technology in any capacity, and the discourse on data privacy is firmly in the mainstream. If you programme your AI to work completely without any biometric data being gathered in the first place, you eliminate the risks of breaking privacy laws, and it also means you’re less at risk when you (inevitably) suffer a breach.
READ MORE:
- Zilch: the time is ripe for a shopping revolution
- Is ‘clicks and mortar’ the answer to future success for pure-play retailers?
- Retail Tech platform, Loyalize, raises £250k in pre-seed round.
- Three reasons retailers need to put their heads in the cloud
To truly unlock the power of AI for retail, we need to focus on increasing education, breaking down stigmatisation, and combining the power of deep-learning technology with human innovation. Once we achieve this, the possibilities are endless.
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