How Intent States work in Plumb5?

Intent Scoring helps in understanding customer’s possible intent towards a product, which can be used in personalizing content or offers based on his intent state. The state can be identified by comparing different behavioral parameters.

These states help the automation engine to pick up relevant messaging and engage the customer automatically. Intent states are used between each stage of the customer lifecycle in order to understand user’s intent to move towards the next stage.

These intent states can be applied to any group created by you to understand customer’s or prospect’s intent within the group. You could quickly visualize your segments by their intent states and roll out a campaign based on their intent

 

The intent scale range is easily configurable. For experimenting marketers, the scoring module allows users to assign their own scores and come up with states using score-ranges.

However, for marketers with no exposure to scoring, they can use the inbuilt algorithm which clusters in real-time arriving at states by using ranges from different parameters.

Plumb5 Unified Customer Model allows the system to easily arrive at intent states for each customer. The algorithm would select each customer data and extract session-level data to get unique pages to total page ratio which is further compared to the recency weights to arrive at the final state.

To explain how the algorithm works, for each customer the algorithm creates a unique variable to understand visitor’s intent by computing total interactions over total sessions.

The resulting output is used to find the first level intent state. For example: If the resulting output is 2.1, then it would push the customer to state-medium based on the following table

If the variable is <2 – Assign State – Low
If the variable is >2 and < 3 – Assign State – Medium
If the variable is >3 – High
The algorithm holds this state and checks for unique interaction count. If the unique interaction count is lower than the desired threshold, there might be a change in the intended state.
If State is Medium and Unique Interaction Count is < 2, change state to Low
If State is High and Unique Interaction Count is < 2, change state to Low
If State is High and Unique Interaction Count is < 3, change state to Medium

These updated states are checked for Recency count. If the last interaction of the visitor has been more than 8 days, then there is a possibility that the states will be updated again.

If State is Medium and Recency Count is >8 then change state to Low
If State is High and Recency Count>8 and <14, then change state to Medium
If State is High and Recency Count>14, then change state to Low

With this, the final intent state is arrived and updated against the user.

Though the algorithm is currently configured for web and app behavior data, with the integration of data containing written feedback or voice data, we can gather the sentiment associated with the latest interaction and use that to change states as well to bring in more context to personalized campaigns.

The algorithm derives the range scale by using the min-max range of the given parameter. The min-max range is arrived by comparing against all user interaction counts. For example, for a particular user, Unique interaction count is 2 and for another user, the interaction count can be 155.  Using least count and highest count, it creates a range scale that is used in comparing individual interaction count for state derivation.

The states can be configured based on the requirements of the marketing team. The algorithm uses a standard three-stage logic (High-Medium-Low) to break the range scale to states. The states can be configured by just specifying the total states needed for your requirement and the algorithm automatically classifies the range scale into the specified state ranges.

This allows in computing intent states in real-time and allows marketers to automate targeting campaigns with higher effectiveness and greater customer satisfaction.

Advertisements

Real Time Analytics and Engagement for Real E-Commerce

In the era of global uncertainty and depressed world economy, one sector has seen some impressive growth trends – that is e-commerce!

Global e-commerce sales reached $1.1 trillion in 2012, up by 21.9% from $893.33 billion in 2011. While North America continued to claim most of those sales. Forecasts indicate that in 2013, total e-commerce sales worldwide will grow by 19.3% year over year to reach $1.3 trillion. China drives most of Asia-Pacific’s e-commerce growth, and is poised to surpass Japan to become the world’s second largest e-commerce market after the United States, this year. India’s e-commerce on the other hand, was worth about $14 billion in 2012, and has close to 10 million online shoppers and this number is growing at an estimated 30%.

This is some good news especially when traditional retailing worldwide is struggling to keep 4% growth rate and many big companies worldwide are pugnacious in defending their market share and profitability.

Continue reading →

Discover how an e-commerce company retained its customers with great customer experience

Background:

An e-commerce company, competing in a tough market environment, was struggling for visitor retention. The site had huge number of visits every day but most of the visitors exited after comparing prices.

An offline and online market research & survey indicated that the website was not engaging enough for visitors and the rewards program made a minuscule effect.
Continue reading →

How real time automation helped increase customer experience and quality of leads for a B2B enterprise

Background:

A B2B services company’s sales team depended on their online marketing activities and website for sales leads.

The company was also facing challenges related to cost of customer acquisition because the leads generated by online activities were often not of high quality. The lead quality also affected their conversion funnel and their typical conversion ratio for website leads was about 2-3% .
Continue reading →