In the realm of e-commerce, conversion rate, the percentage of website visitors who make a purchase, is often touted as a key performance indicator (KPI). However, this metric can be misleading, as it implies that every website visitor can be persuaded to buy. This notion is far from reality.
The introduction of the “sales funnel” concept highlights the need for a more nuanced approach to customer conversion. By understanding the different stages of the customer journey, businesses can tailor their marketing strategies to effectively guide users towards a purchase.
Unfortunately, many e-commerce platforms lack the tools to analyze user behavior and personalize the customer experience accordingly. This is where a groundbreaking solution emerges – a platform that empowers businesses to analyze user behavior and deliver targeted interventions based on the user’s position within the sales funnel.
The concept of a personalized sales funnel optimization platform holds immense potential for revolutionizing e-commerce conversions. By leveraging user behavior data, this platform can dynamically adjust marketing messages, product recommendations, and website content to align with the specific needs and interests of each individual user.
To illustrate the viability of this concept, a working prototype has been developed. This platform demonstrates the ability to track user behavior across the sales funnel and deliver personalized interventions accordingly.
The market for a personalized sales funnel optimization platform is vast and untapped. With the ever-growing demand for e-commerce solutions, businesses are constantly seeking innovative ways to enhance customer engagement and boost conversions.
The development of a personalized sales funnel optimization platform presents a unique opportunity to capture a significant share of this burgeoning market. With a robust solution and a well-defined marketing strategy, early entrants can establish themselves as industry leaders and reap substantial rewards.
For entrepreneurs and tech innovators seeking to disrupt the e-commerce landscape, the development of a personalized sales funnel optimization platform offers a compelling opportunity to make a lasting impact. Join the forefront of this transformative technology and revolutionize the way businesses convert website visitors into loyal customers.
Project Essence
Made With Intent helps online stores better convert their website visitors into customers. But not by forcing stores to pressure visitors into buying something right away, but by helping stores understand who the people are and why they came to their website.
The catch is that buying something more expensive than a penny chewing gum at the supermarket checkout is a multi-step process. It’s no wonder terms like “sales funnel” or “customer warming” have emerged. People first look around in general, then narrow down their options and explore them further, then hesitate, then add the item to their cart but leave to think, and so on – until they finally make a purchase or reject it. And at each of these stages, you need to influence people differently to push them further along this sales funnel.
Despite this, most tools attached to online stores to stimulate purchases assume that people who come to the site are ready to buy something right now. Therefore, they try to influence everyone in the same way, which can only lead to a purchase for those who are already ripe for it. And the positive result is considered only conversion into a purchase during the current visit.
However, Made With Intent’s AI engine tries to understand in real-time why a person has visited the store’s website. To do this, it analyzes over 200 signals related to what a person is looking at on the site and where they click. It not only analyzes current behavior but also what the person did on this site during previous visits.
All this is done in order to classify the visitor into one of the segments based on their position in the sales funnel. This segment can change in real-time – depending on where the visitor clicks or what other behavioral signals they send.
With this information at hand, the website owner can customize the display of different ads and special offers for visitors belonging to different segments. And this more effectively drives visitors towards making a purchase.
Although, maybe not during this visit! The startup claims that using the platform can increase the store’s revenue by 9.4% within 6 months.
A “segment” is essentially a set of behavioral signals from a visitor that the platform’s AI engine captures. The platform already has a set of ready-made segments, but the owner of an online store can create their own segments to further finely and precisely adjust the impact on visitors.
Among the ready-made segments are:
“Undecided” – those who are really ready to buy but cannot make up their minds, continuing to explore options and conditions. So they need the final push, like a discount at the time of purchase, which can overcome this indecision. “Just Browsing” – those who need to be shown some unusual product or an unusually compelling offer that can suddenly catch their attention, causing a sharp change in their behavior on the website. “Not Decided for Some Reason” – people showing a high intention to purchase a specific product but leaving the product description page without adding the item to the cart. They can be shown some information or a special offer that will bring their thoughts back to this product.
Moreover, you can properly influence a visitor who falls into a certain segment not only on the store’s website itself! You can automatically and regularly export user segments for ad retargeting – showing suitable offers to these users even after they have left the store’s website for their favorite social network, for example.
Additionally, by automatically segmenting website visitors by their readiness to make a purchase, conclusions can be drawn about the overall quality of traffic coming to the site. And based on this, external advertising content and channels used for it can be optimized.
To start using the platform, it is enough to spend a couple of minutes adding just one script to the store’s website – which will immediately begin to analyze visitor behavior and segment them.
The Made With Intent startup was only created this year, and its platform is still in the beta testing stage. But it already has real customers who have started to derive real benefits from using the platform.
As a result, the startup has now raised its first £1.5 million (approximately $1.88 million USD) in investments.
What’s Interesting
Analyzing user behavior on websites is the focus of the startup Session AI, which I wrote about in the fall of 2022 when it was still called ZineOne. They created a platform that, with just 5 clicks on the website, can guess what a visitor wants to buy, even if nothing is known about them. Session AI has raised $43 million in investments.
The problem is that 90% of visitors to online stores are anonymous – they either never registered on the site or their login has already expired. Therefore, the store cannot offer them anything on the fly based on their profile or purchase history – and that’s why stores have to resort to platforms like Session AI for help.
The startup Neocom, which I wrote about in the fall of 2023, also analyzes user behavior on the seller’s website. Its goal is to understand when a person starts to experience difficulties in the process of searching or choosing a product, so that a digital assistant can be sent to them at that moment to ask questions and provide additional information. This startup has raised $4.5 million in investments.
The catch of these startups, like today’s Made With Intent, is the AI engine that can analyze user behavior on the website. Although in this way, user behavior can be analyzed not only for online store shoppers.
The startup ForMotiv, which I wrote about in early 2023, has created an AI engine that analyzes user behavior when filling out forms on insurance company websites. In this way, it assesses the real intention of the user to conclude a contract or even that they are concealing something – which allows insurance companies to better qualify applicants. This startup has raised $9.4 million in investments.
And in January of this year, I wrote about the Y Combinator graduate Subsets, which analyzes the reading habits of internet publication subscribers and segments them according to these habits – allowing the publication owner to influence them differently.
However, the catch of these startups is not only in analyzing user behavior. But in the fact that as a result of such analysis, owners of internet services stop measuring their visitors with the same yardstick and, as a result, influencing them in the same way.
After all, pinpoint analysis of each user allows for pinpoint influence on each of them – resulting in a much better cumulative effect. That’s why Made With Intent called themselves the “Moneyball for e-commerce.”
If anyone doesn’t remember, “Moneyball” is the title of a book and a movie made from that book. The book describes the true story of the coach of the “Oakland Athletics” baseball team.
Before him, the strategy of baseball teams was to attract stars showing the highest individual results. But “Oakland Athletics” lacked the money for this, and besides, their stars left the team. And then the coach accidentally met a talented mathematician who created a mathematical apparatus for evaluating the effectiveness of baseball teams.
The mathematician argued that good results could be achieved even without stars – by choosing average players based on static data and placing them in the game properly. The coach followed his advice, recruited seemingly unpromising players to the team – and his team won an unprecedented twenty consecutive victories, setting a record in the American League.
Where to Go Next
AI now allows real-time analysis of user behavior on websites, capturing hundreds of diverse signals and millions of micro-events. In the case of ForMotiv, which analyzes form submissions on insurance websites, this includes even mouse movements and keystroke speed.
The result of such analysis is a highly detailed segmentation of users into micro-segments. This information can be used to make subsequent decisions or to automatically interact in a special way with each of these segments in real-time.
These technologies have become available relatively recently and are still evolving. This means that they are not yet applied everywhere where they could be successfully used. And where they are already being used, there is still room for exploration 😉
So, there are two possible directions to move in.
The first direction is to create analogs of already existing platforms working on this principle. Taking, for example, one of the startups mentioned today as a model to emulate.
The second direction is to consider in which other areas and for solving which tasks the same approach with automatic analysis of user behavioral signals and subsequent automatic segmentation can be used. By the way, it occurred to me that this approach could be called “Divide and Conquer” 😉
In which other areas is it worth stopping considering users as a homogeneous mass of people with the same intentions? Into which segments or even micro-segments can they be divided? Based on what behavioral characteristics can this be done? How can different influences be exerted on each of these segments to achieve the best result? How can this be technologically implemented?
About the Company
Made With Intent
Website: madewithintent.ai
Latest Round: £1.5M, 02.05.2024
Total Investments: £1.5M, Rounds: 1