Jonathan Pitcher: “Ysance Stories empowers marketers to know what to sell, to whom and the best way how.”

Jonathan Pitcher, VP Marketing Ysance.


Driving marketing performance with the ability to always know what to sell to real purchase intenders and the best way how, for both recognized customers and anonymous visitors alike, online and in-store. A pipe dream?

Not for brands using Ysance Stories, a contextual marketing engine that makes it possible to analyze and influence shopper journeys, as they unfold, automatically, and at scale.

Ysance Stories was launched in January at the NRF show in New York. At the same time, Ysance, with offices in Paris and San Francisco, entered the Gartner Magic Quadrant for Digital Marketing Hubs. I sat down with Jonathan Pitcher, VP Marketing of Ysance.



:: Tell us about Ysance Stories in a few words

Jonathan Pitcher: Ysance Stories is a revolutionary approach to contextual marketing. It uses artificial intelligence to analyze and influence omni-channel shopper journeys as they unfold. In other words, Stories can recognize purchase intent and, in turn, influence these shopper journeys by recommending the next best marketing action. The results are phenomenal. It is a true driver of revenue.



“Affinity is meaningless in itself”


:: How did you achieve this?

Stories came about through the efforts of our product team and through conversations with our customers. We came to the conclusion that notions of “buyer signals” and “customer journeys” as currently defined by the market are incomplete.

What do we mean by purchase intent? Many solutions score interest. However, interest is not the same as intent. Interest or affinity is meaningless in itself. Let’s say I’m a musician and I’m interested in bass guitars, it doesn’t necessarily mean I’m currently in the market to buy one.

The same goes for the customer journey: it’s a construct that can be seen in different ways. In reality, a single shopper journey can contain multiple purchase intents, for different products, or for different people (e.g. for my children, a gift for my mother-in-law, etc.). Until now, marketers have been reliant on guesswork or, for the more advanced, on ad-hoc scoring or behavioral rules. Ysance Stories moves beyond this.


“Intent must be analyzed separately from the journey”


:: What do you suggest?

Current solutions that deliver a list of people interested in a product that are based on ad-hoc rules are following a flawed approach. Not only this, they do not sufficiently leverage the retail context. Retailers have access to offline data and to their physical stores network, which hold a key to the solution if they are used correctly.




Ysance Stories uses artificial intelligence to analyze all online and offline customer interactions (outbound campaigns, click streams, mobile applications, POS data or data from tablets in-store, etc.). Stories learns and adapts to the different types of purchase patterns and then detects purchase intent for both recognized customers and anonymous visitors alike.


At the level of each individual purchase intender, the engine understands where they are along the purchase path and their probability of conversion. It produces a slew of personalization parameters that can be used to optimize marketing engagement. These include a prioritized product selection, channel recommendation (email, display, mobile notification, sms…), price bracket, likely conversion channel (online or offline) and transaction values.


Because the system maintains a persistent, privacy-compliant view of individuals over time, and because we are capable of integrating point-of-sale data, we can evaluate the contribution of each and every interaction to the final conversion. We can also provide access to customer journey analytics at the store level. This means that store managers, supply chain managers and the like have access to predictions of in-store demand based on cross-channel interactions. This is extremely valuable for merchandising, inventory optimization, and point-of-sale planning decisions in general.


:: Why use artificial intelligence?

In the same way as in medicine, an AI engine can deliver the right diagnostics by analyzing thousands of radiographs in one microsecond – an expertise that a seasoned radiologist will have taken 40 years to build – our AI engine will analyze the whole of the company’s online and offline data – not just those that the human keeps in mind – and return the best product-price bracket-channel recommendation at that particular time, for that buying story. Simply put, AI can handle extremely complex and varied sources of data and can do this at scale.


Our AI engine analyzes all available online and offline data and produces next best marketing offers that consist of optimized recommendations of product, price bracket and channel at a given moment in time for all open purchase journeys or ‘stories’.

Unlike the marketer, our AI engine does not need to be given prior assumptions; it is self-learning: It learns from both online and offline data and adapts continuously to changing conditions. However, we’ve designed the system to help marketers, not replace them. Marketers retain control over how they use this segmentation data and personalization data. They are free as how they use it, to filter it, to overlay or substitute their own conditions to take in account the local context, weather conditions, their own promotional priorities, etc. The final decision is their own.


:: What does Stories look like?

Visually speaking, Stories provides retailers with a dashboard, which represents an “in-funnel” view of potential sales by category or brand, showing their progression through to conversion, based on similar buying patterns which resulted in a sale.



Let’s take as example a mass market sports retailer. The engine will look at all visitors on the website, both recognized customers and anonymous visitors alike, and once a visitor interacts with a product category, it opens a Story. Each Story will have different rules, depending on the product’s sales cycle.

If after a certain time (which is also calculated by the engine based on buying patterns), no more interactions are detected for that particular product category, the engine will close the story. On the other hand, continuing interactions are used to refine the picture of purchase intent.




:: What are the best use cases?

The use cases are simple and they all drive revenue both online and in-store, since Stories leverages Ysance’s superior people-recognition capabilities which reconcile anonymous visitors and their behavior with known customers, their CRM attributes and their purchase data. With a unified view of online and offline identities and purchase paths, retailers gain the ability to:

  • drive additional revenue both online and in store all while optimizing marketing spend,
  • measure impact of campaigns not only on online conversions but also on in-store transactions,
  • predict in-store demand by destination and by time and highlight trends to help with planning and inventory optimization.


“With Stories, a retailer is enjoying $17 of incremental revenue by email opened, for an average order amount of $60”



:: What results are you obtaining?

Ah! The million-dollar question! With the ability to activate your data, you gain great leverage (and we are only at the beginning!) So, let me share two figures with you:

  • an international retailer is enjoying $17 of incremental revenue by email opened, for an average order amount of $60,
  • another major player in the specialty retail sector is seeing a durable improvement to its email open rates, which now attain 36% because its messages are more relevant. Needless to say, this is also positively impacting revenue per email metrics.

But that’s just a taster! There are many other compelling metrics that we can share on request.




:: Stories is people-based and reveals intent, rather than starting out, as other solutions do, from a product in order to find the customers to target

That’s what sets us apart from the market. Stories reveals individuals with their multiple purchase intentions. Marketers gain the ability to act on these intentions and to rewrite their outcomes because they know what to sell to whom and the best way how, at any given time, across all channels.


“Considering the shopper no longer as an object but as a subject”



:: At the end of the day, isn’t your predictive approach at the individual level cumbersome for retailers?

On the contrary! Today’s consumers have become masters in the art of storytelling, sharing their own stories on social media. What does storytelling teach us? That a story is a hero that goes from A to B with an intention – for example to save the princess – but an obstacle arises their path – say a dragon. Then along comes a helper!


In marketing terms, this is a change from push marketing. It’s a change to helping the customer. To friction out of the buyer journey. A “Go here. Buy this” marketing message is treating the customer as an object not a subject. The consumer wants to fulfill a desire, but obstacles get in the way (article not available in store, promotion only valid online, the retargeting message was sent after the actual purchase, lack of cross device consistency, etc.)

For the marketer, Stories is the helper that helps the shopper complete their purchase path. Stories detects purchase intent and helps the brand “write” the winning outcomes. The customer is the hero. Stories is the helper!


:: We are currently experiencing a backlash against retargeting and against black-box techniques that consumers don’t understand or control. What is your take on this?

Precisely, Ysance Stories is not a “black box” in the sense of giving you information that you don’t know the origin of or what it’s been mixed with. Stories is an engine that works exclusively for you and on your own data. It produces predictive insight based on your first-party data alone.

Stories makes for better marketing by addressing only true purchase intenders who by definition will respond to your messages better than those with no interest.






Les commentaires sont clos.