What makes us unique is that we study the psychology behind decision-making. We apply these principles in the digital marketplace to help people buy with confidence.
Instead of setting us free, mounting options quickly overwhelm us.
Often, we simply can’t choose at all. And even if we make a good choice, it’s easy to imagine we left that one, perfect option on the shelf.
Your customers face this every time they visit your site. And online there’s no sales assistant to turn them into a confident buyer.
So, what’s the answer?
Customers are more likely to engage if the offer of assistance aligns with their key goals and motivation. Think less “Hi, can I help?” and more “I see you’re looking at…”.
A customer is only likely to act when they have an aligning goal. For example, an “Eat Popcorn” ad at the cinema will only impact hungry customers.
It’s necessary to understand a consumer’s motivation in order to align with their goals and gain trust.
When people identify a gap between what they currently know and what they would like to know, they’re motivated to seek further information. To keep consumers engaged, it’s necessary to identify this information, then present it at the right time.
Our engine monitors each customer from the moment they land on a site, building up a set of behavioural traits.
Those traits are then used to create a tailored engagement strategy – ensuring that every conversation starts with a message that aligns with each customer’s goals & motivation.
No two customers are the same, so you need to create conversational experiences that adapt and reflect each customer’s behaviour and tone.
Once a customer starts to engage in a conversation, they are more likely to complete the process and make a sale.
Rapport can be built in a conversation by subtly mirroring aspects of the other person’s verbal behaviour and tone.
The more a consumer is engaged in a sales process, the more likely they will be to finish it and buy the product.
Once a customer has agreed to a small request, they are more likely to agree to a larger request.
Customers are more likely to trust things that are easy to look at and simple to understand.
Adapting to each customer, the engine creates a naturally evolving conversation. It speaks everyday language, and works to build the customer’s trust and confidence.
It asks a few questions to find out who they are, then tailors a journey to suit. The result is an average journey completion rate of 92% – and a sale.
A customer is more likely to purchase if they’ve received a personal recommendation. An in-store assistant learns and improves over time, so your digital channel needs to do exactly the same thing.
When a consumer is offered products that match their personal motivation, it will increase the likelihood of a sale. Therefore, it’s crucial to find a reliable way to understand each customer and discover their individual motivations.
People learn through observing others’ behaviour, attitudes and outcomes: “Most human behavior is learned observationally through modeling: from observing others, one forms an idea of how new behaviours are performed.”
Albert Bandura, Social Learning Theory
We use a series of intelligent algorithms to understand a customer’s underlying preferences and behaviours.
We then cluster them with similar and known customers, allowing us to present products with the highest conversion rate to like-minded customers.
The key to making a confident buying decision is understanding why a product is right for you. A customer is more likely to purchase if they feel that they’ve been understood, so it’s important to
communicate why a product meets their core motivation.
However, messaging should appear as helpful and neutral, not designed to persuade.
You can increase confidence in a recommendation by repeating back information that’s been given by the consumer. This should capture what was said while showing a wider understanding.
When a consumer is offered products that match their motivation, it increases the likelihood of a sale. Therefore, it’s crucial to find a reliable way to understand each customer and discover their individual motivations.
Conducted during World War Two, a famous Yale University research project into persuasive communication showed (amongst other things) that messages should not appear to be designed to persuade. Since then, numerous other studies have backed this up.
Due to the conversational nature of the engine, we are able to build up an accurate set of traits about each customer, including their key decision drivers and motivations.
The engine then uses these traits to cut through generic product descriptions - providing compelling and personalised USPs that resonate.
A consumer’s propensity to purchase can be heavily influenced by the behaviour of others.
The impact of this effect is influenced by the level of uncertainty a consumer feels, the volume of people enacting the behaviour and the level of affinity a consumer feels with those people.
If a consumer is uncertain, their behaviour can be heavily influenced by others. This effect is heightened if they feel familiarity with the other people who are enacting that behaviour.
A consumer may change their behaviour in order to satisfy a need to conform to a particular group – even if they feel strongly that it is not their preferred behaviour.
Because each recommendation is the highest converting product across like-minded customers, the engine is able to provide data- backed social proof – showing how popular the product is with similar customers and providing relevancy match scores.
Customers buy with greater confidence when they feel they’ve made an informed choice. Confident decisions are primarily made by contrasting a primary item with alternatives.
The type of alternatives used to frame a primary recommendation can directly influence the perception a consumer has of that primary product.
Customers are persuaded to choose a middle option when it is positioned between a more expensive option that they don’t need and a cheaper option that does not meet their
requirements.
When consumers make decisions, they tend to do it by contrasting the decision item and reference items. It’s difficult for consumers to make confident decisions without having experienced a comparison.
The engine puts the final decision in the hands of each customer. Their ‘perfect match’ is framed with alternatives that have proved effective at building confidence in the primary recommendation.
Personalised comparison is provided by displaying tailored USPs and match scores for each option.
Too many choices can reduce the likelihood of a decision being made and increase dissatisfaction. You can improve conversion by using personalisation to filter out irrelevant products and provide a
reduced set of targeted options.
The same is true for upsells – with consumers more likely to buy an additional product if they are presented with a limited and personalised set of options.
Too many choices can reduce the likelihood of making a decision and increase dissatisfaction and anxiety. Consumers are also more likely to regret a decision if they were faced with a large number of options during the decision cycle.
To increase the likelihood of making a sale, it’s important to offer a focused and simplified set of choices.
Insurance and accessory take-up rates are far higher in retail stores than online. This is because the in-store assistant is able to personalise the offering, bringing a targeted option into conversation at just the right moment.
Our engine replicates this craft, displaying an upsell offer just before going through to basket and using traits data from the conversation to reduce and personalise choice.
During a recent trial, the engine drove an uplift in insurance sales of 59% when compared to the control.
