Blog Post

It’s time to make marketing an exact science

Customers' habits and preferences are shifting to favoring ecommerce, which has only continued to grow as the primary form of shopping. The impact on brands is reflected in the increase of online marketing practices, especially post-pandemic, worldwide...

It’s time to make marketing an exact science


Alegria Adedeji

Mar 7 • 10 mins read
It’s time to make marketing an exact science

The current challenges

Customers' habits and preferences are shifting to favoring ecommerce, which has only continued to grow as the primary form of shopping. The impact on brands is reflected in the increase of online marketing practices, especially post-pandemic, worldwide.

Currently, we see three core challenges impacting the effectiveness of brands’ marketing:

1) Reliance on third-party data

The ecommerce marketing landscape is extremely competitive and expensive to maintain an upper hand against competitors. However, marketing budgets have also been impacted, meaning marketers are expected to do more with less. But brands are all working to memorably personalize and advance the online experiences of prospective customers just using cookies and other third-party data.

Soon, a reliance on cookies alone won’t be possible within ecommerce marketing. With data policy and sharing rules on ad networks changing, brands need better, more precise ways to connect and engage with audiences. The key to more exact marketing? Harnessing the power of first-party data. The solution isn’t in this alone, however; the ability to action learnings from first-party data is hampered by siloed data and tech stacks. This means making ‘real-time’ truly personalized experiences will still be a challenge.

2) Limited digital transformation

Digital transformation is everywhere. There are often reports of high-street brands closing as the number of new and heritage brands adapting to the shifting tastes increase. But the ability for true digital transformation is a challenge in and of itself. Data and ‘real-time’ decision making are the key to long-term scalable success, but ecommerce marketing in its current form is not built off this foundation. This manifests in a lack of data-led campaigns, low sell-through rates and a reliance on annual discounting events.

Now, more than ever, marketing needs to be treated like an ‘exact science’ to be able to keep up with hyperscale retailers and drive sustainable growth for the future. Brands should have an in-depth understanding of its customers’ lifecycle, and the ability to predict propensity and replenishment; ensuring hyper-personalization as the standard.

3) Intensifying competition

A select number of hyperscale retailers dominate 57% of all global online sales. This makes for even more pressure in ecommerce teams; along with the growing expectations of customers, often shaped by these big retailers, and the need for the customer experience to feel tailored to each shopper who engages with your marketing.

But this is only the beginning of the next stage of ecommerce. There is ample opportunity for smaller brands, and especially those willing to make their marketing an exact and precise science; harnessing the power of first-party data to empower hyper-personalization, forecasting and a longer CLV.

The secret to the biggest retailers’ successes

So, despite the competitive landscape, how are these retail giants able to scale and sell so quickly and profitably?

Answer: Harnessing the power of ‘First-party data’ and Reinforcement Learning

First-party data alone won’t make for advanced marketing, just as having a CRM won’t give you a complete picture of your shoppers. Stack health continues to be a priority and the tools you use need to be able to take data, action [it] and continue to optimize to ensure your marketing feels tailored and relevant.

A Customer Data Platform (CDP) collects and unifies first-party data about your customers. This helps to create a streamlined and unified picture; this information can come from transactional, demographic or behavioural data.

Hyperscale retailers are using that first-party data as the fuel to drive the engine of their operations. This is advanced by using a Reinforcement Learning Tool (or Collective Artificial Intelligence). Retailers can teach the AI to learn about its customers’ habits and to learn how to optimize and encourage revenue. The CAI then extends to other areas of the business, including operations and stock management, by limiting or increasing amounts needed based on continually-learning algorithms. This is the key to their scalability and profitability.

And with large financial resources, most of these solutions are built in-house or are custom stacks to execute campaigns. This duo is how they enable business growth, customer loyalty and lifetime value, year-on-year. And Cloud.IQ wants to make it available to all retailers, regardless of size.

The current solutions (aren’t good enough)

The majority of ecommerce brands have two options when working in the current ecommerce space:

1) Buy expensive tech which is unaffordable for most merchants and may potentially take months or even years to fully integrate.

2) Piece together different tech-stack tools to solve different challenges, but these may not work well together and increase workload or inefficiencies.

What’s currently on offer for most brands?

Amongst marketers there are frequent debates about which tech and data stack would lead to the most cohesive and successful customer experience. Some brands opt to work with multiple tools which all have specific focuses and a different role to play. This might be a CRM which is bolstered by a CDP, a personalization engine, A/B testing platform and an ESP to send out those messages. This option may be quicker to get up and running as well as easier and less time-consuming to get to grips with; especially for smaller to mid-market brands who want a smooth introduction to digital transformation. But this means multiple log-ins and more time spent doing repetitive tasks. The differing tools may also struggle to ‘talk to’ one another leading to less actionable data and a fractured image of customer audiences and online experiences. The product roadmaps for the differing tools may also be dictated by larger customers’ needs, so the solution, after having integrated it into your stack, may end up not working for you at all.

Larger brands may opt for full suite platforms which have the differing stack elements or most of them in-built and, budget permitting, tailored to their specific needs. These solutions however are expensive and can take up to a year to integrate. This looks like a time-to-value that may be upwards of 18 months and continuing to become more expensive year-on-year. Due to the infrastructure being complex, these solutions are less flexible at adapting to changing tastes and needs of customers and the marketing space, further complicated by a potential need to have select ‘super users’ who are relied upon to make specific changes.

What’s possible with Cloud.IQ?

Marketing within ecommerce is both an art and a science. It requires the knowledge of what looks great, but equally how to make sure that great marketing creates conversion.

At Cloud.IQ, we believe that marketing can and should be precise without skimping on the quality. With our ‘AI as a service’, you’re able to empower Reinforcement Learning to continually optimize campaigns as your audience grows and matures, all without having to switch to a brand new platform. Being driven by targets you set, means that our AI models help you to create conversion-friendly campaigns and find the most effective way to create minimum wastage in your marketing.

Reinforcement learning can take you steps further than typical ‘hold tests’, which are the methods marketers use to try out theories that may lead to conversion, and then manually optimize them. With AI as a service you can test every possible configuration of messaging or engagement to get first time and repeat buyers, while constantly optimizing in the background to suit changing customer preferences and the marketing environment.

With our microservices architecture, we make it possible to use only what you need from us to better your marketing and data stack. So, how is this different from the bolt-on solutions mentioned earlier?

Our offering is ‘API-first’, which means you can seamlessly integrate the tools you do have to our advanced AI models or use us as a CDP for your first-party data. You maximize your marketing best when you use the platform in its entirety - but your engagement quality will be high, regardless.

Using our platform you have access to ‘Identity Resolution’, working as the north star for your campaigns and ensuring effectiveness. Without having a clear picture of who you’re serving, your marketing is all art and no science. Ecommerce also goes beyond, and before, the point of sale. With our ‘Demand Forecasting’ you have a 360 field-of-vision of the entire organization; further empowering you to create sustainable growth and impactful engagement.

Hyperscale retailers are currently using reinforcement learning to assist their huge growth and influence over ecommerce. Digital transformation has already begun and the time to adapt your ecommerce strategy is more pertinent than ever. We’re here to make this technology more widely available; helping you put in place an evergreen, sustainable and ‘exact science’ approach to your marketing and ecommerce.