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.

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Everybody knows silos are bad for business

It is evident that 99% of businesses, irrespective of big or small, is bleeding heavily due to data silos. The current environment demands huge dollars to be spent in order to maintain data, which covers paying for redundant data and isolated data due to data sources that don’t provide a common unique parameter for unification. Data grows on a daily basis and there is no escape from the cost incurred on growing redundancies and data silos. This sub-optimal approach not only costs a lot of money but also create gaps in achieving the desired results.

Problems associated with data silos can be many. For example, if you are facing the problem of higher customer acquisition costs, you can see that this is due to (a) inaccurate attribution models leading to ineffective ad spends and (b) lack of customer engagement or irrelevant targeting. Both these problems are resultants of data available across many data sources and lack of unique tags to merge them. So, the analysis carried out over individual data-sets results in wrong analysis, which results in low effectiveness and wasted resources. Continue reading →

Explainable AI: Visualizing and Interpreting Machine Learning

Machine learning algorithms operate in a so-called black box — so the inputs and outputs are known, but how or why an algorithm makes the recommendation it does is not clear. Despite the AI media frenzy, companies and governments are concerned about the machine learning black box. The EU’s General Data Protection Regulation, which takes effect next year, includes a right to explanation clause, or an explanation of how a model made a decision.

The Explainable AI (XAI) program aims to create a suite of machine learning techniques that:

  • Produce more explainable models, while maintaining a high level of learning performance (prediction accuracy); and
  • Enable human users to understand, appropriately trust, and effectively manage the emerging generation of artificially intelligent partners.

New machine-learning systems will have the ability to explain their rationale, characterize their strengths and weaknesses, and convey an understanding of how they will behave in the future. The strategy for achieving that goal is to develop new or modified machine-learning techniques that will produce more explainable models.

Continue reading →

Investing in Customer Service Robots?

Planning a customer service robot for your business? We are sure that you would not want your robots to dispense generic information. You would want your robots to personalize and engage with customers, more like humans, understanding user’s preferences, past behavior, products to recommend and other factors. And your robots will need access to a single customer file that loads all relevant information in real-time.

If you already have a Plumb5 account, then you can get the single customer information(JSON) which can be integrated into the interfacing robot apps for Ginger, Pangolin or other. When the service robot engages with the customer, the entire transcript or menu navigation can be updated back to Plumb5 to ensure the latest customer interaction is updated for next set of insights

If you have invested in a customer service bot, you can contact us here and we can set up a call to walk you through the 4 step integration process

Accelerate your business with real-time insights

 

Plumb5 offers a full breadth measurement platform to measure end-to-end consumer insights helping businesses grow by making faster, smarter and better decisions.

Plumb5 allows marketers to measure engagement outcome of their customers —individually. With a new level of precision and auto-segmentation features, you can quickly identify and drive automated customer engagement, thereby increasing relevance and providing a better experience to each of your customers.

Plumb5 allows marketers to measure engagement outcome of their customers —individually. With a new level of precision and auto-segmentation features, you can quickly identify and drive automated customer engagement, thereby increasing relevance and providing a better experience to each of your customers.

End to end marketing requires consumer insights at all stages of the lifecycle. Insights at each stage allow the marketer to optimize their communication to achieve conversion goals. Plumb5 Customer Management presents the complete omnichannel journey starting from the time they have clicked through an ad for the first time to their current state of engagement. Plumb5 is designed to reach the consumer at the right time(real-time), at the right place (channel) with the right product experience (relevance in marketing), enhancing marketing effectiveness

Here is a list of consumer insights that are available for the marketer to will help marketers make quick, smart and better decisions

Engagement Optimization

Plumb5 Platform creates a unified view of a single customer so that marketers can visualize the complete journey of customer interactions across all channels. With this unification, the marketer has access to insights such as

  • Product related customer preferences covering product and category affinity, sentiment tags gathered from social comments or in surveys, purchase frequency and other tags like sub categories, objects, color preferences.
  • Channel Preferences would reveal the most active channel for communication along with time and day of the week to communicate
  • Demographic and Social Data would reveal the user’s public social handles, along with Name, Gender, Age, Marital status and other available demographic data.
  • Customer Omni-channel Journey would chart the entire journey of the user across all channels, sequenced by time. This reveals the present customer state and their engagement details at each stage and every channel.

 

  • Behavior Scoring allows marketers can set scores for interactions which will help them to automate engagements whenever the user achieves a new score.
  • Pattern Predictions allows the marketer to visualize the current state of the user and predict possible goal conversion paths, which helps marketers reach conversions in shortest possible paths
  • Using all the parameters or preferences of the user, the recommendation algorithm serves insights on what works best for that single user.
  • After these recommendations are fired, the responses from the user are recorded and updated to the stack, which remodels and presents the recommendation engine with new tags.

Using these tags, marketers can automate using these insights to propose the next best communication to a particular user and automate their conversion campaigns for higher efficiency and faster conversions

Advertising Optimization

The marketer can further look at insights on which advertisement generates deeper engagements and their influence over purchases. Marketers can use custom scripts or allow pre-tag customization to ensure ad response data is integrated to Plumb5, allowing them to measure customer journeys right from the time they responded to an advert.

Plumb5 Omni-channel Tag management allows the marketer to understand the flow from the advertising source sites and measure which of your media is paying off. Plumb5 Probabilistic Attribution Model attributes the customer journey to each of these ads to understand the influence and returns for that campaign spend.

Ad Measurement and Effectiveness Report shows insights on how many ad-clicks landed on the site/app, the depth of engagement created with these ads, leads generated through these ads as well as successful purchases directly or indirectly influenced by the ad.

Along with this measurement, you can get to setup an automation workflow to convert these first-time visitors or anonymous users coming from ad campaigns, to ensure the users visiting from these ads are engaged and moved up the conversion path. This ensures better ROI for the marketing spend and optimizes customer acquisition costs (CAC).

Content Optimization

To understand how your engagement content fares, you can ready insights on Plumb5 to understand which banners, templates or landing pages are working for you and allows you to test your content to understand effectiveness before you take it to the target audience.

Be it a web page or a mobile app or an email message or any other digital template, Plumb5 tags allow you to record responses and understand the effectiveness of the campaign over an individual consumer.

Metrics that you can quickly access would entail

  • Page and Event Tracking to understand the behavior of the user on websites, e-commerce sites or on landing pages
  • Event and Gesture Tracking allows marketers to understand app user’s behavior
  • Heat maps give an overall interaction hotspots on a particular page or an app screen
  • A/B and Multivariate Testing allows you to test content variations to understand what works best
  • Sentiment Analysis can be used to measure the tone and sentiment in customer responses
  • Chat bot Tracking allows you to track chat conversations of the customer with the bot and generate key insights of the conversation.

Purchase Optimization

Plumb5 integrates with offline and online transactions to draw a full picture of the customer journey all the way till the point of purchase. Using predictive algorithms, businesses can gather a whole lot of insights regarding behavior and experience of a customer, product placement, forecasts and  pricing strategy for each consumer based on unit economics

Some of the main insights that are covered that enhances purchase optimization

  • Customer Satisfaction
    Customer experience, both at and after purchase, is an important aspect of the brand’s promise to the customer. Plumb5 allows the marketer to extract insights from engagements and understand key metrics like; how satisfied are customers with products or services; do customers find a value for the money they pay?; does their behavior alert a shift to a competitors product?; how loyal are they to the brand, along with overall responsiveness towards the products or services.
  • Market Basket Analysis
    Plumb5 allows marketers to visualize segments pivoted around SKUs to understand the assortment of products that a particular segment is most likely to buy. You can tag these high weighted results to the personalization engine so that they can be recommended as part of your cross-sell campaigns.
  • Store Behavior
    With beacon implementation, the integrated insights can reveal a lot about a customer at the physical store. Customer walk-streams, Browsed racks, Merchandising insights and other customer interactions that can help you strategize to better customer experience and improve sales
  • Forecasting
    The integrated data allows for accurate predictions of various factors in a business. From predicting favorable parameters to launch a product to predicting the sales of a specific product during a specific season or to predicting sales and revenue from a particular zone or customer, the pattern detection algorithm can be employed to predict scenarios and also prescribe the shortest paths to manage over predictive outcomes.
  • Unit Economics:
    This helps the business manager to quickly check each customers profitability status for a given time and help in planning targeted communication and campaigns.The app was primarily designed to handle high volume personalization workflows where retention campaigns can be executed in real-time based on current value of the customer. Rules and conditions can be configured to execute campaigns for customers who fall in respective revenue clusters. The app also reports each customer’s performance metrics comprising of Frequency & Recency, Transactions, Risk,  Purchase cycle, Response Frequency, Cost of Retention, and Present Value

The Proof of the Pudding …

Everyone knows that the real value of something can only be judged from practical experience and results; not from appearance or theory. And that’s exactly how one should measure any AI implementation – from the outcome of its application.

This is essential to clearing the hype around AI and focusing on methodologies that drive pure value. We believe that AI is the way forward and to substantiate, we’ve put together a case study to showcase how AI can bring true value to your business processes.

The Client: A global retail chain which sells products, both online and offline.

The Implementation: A behavior scoring model to generate machine driven recommendations, in order to resell/upsell products and drive revenue.

The Test: After six months of implementation, we ran up a test to understand how the logic fared. We filtered out data of all machine based recommendations that were triggered by specific customers, and see if they purchased the same product as recommended within a timespan of 0-5 days.

The below chart shows the outcome of the recommender logic.

 

The Results:
A total of 184,712 users responded to the recommendation, which is broken down by the number of days.
33,358 users purchased in less than 24 hours.
32,080 users purchased within 24-48 hrs of the recommendation being sent.

The machine had recommended about 2,000+ unique products to 20,000+ unique customers, based on their behavior. It generated revenues in excess of $200,000, every day!

This is proof that AI based Recommender Systems can make a big difference in the way businesses channelize their revenues and contextually engage their customers.

About Plumb5 Recommendations
The recommendation module connects to the unified data model on Plumb5, where data is primarily classified by users.  On every interaction, the algorithm creates and maintains an associated product list against each product, which gets updated after each relevant interaction, all in real-time. Read more about Plumb5 Recommender here

How does it work?

For a machine to personalize and drive the user to the desired goal path, it would need to match and deduce the next best event and target message that will drive him to the goal path. This computation needs to be done in real-time in order to personalize instantly when the user is active.

This real-time computation would require (a) appending current session behavior pattern to past behavioral pattern of the current user and (b) the deviation of the current user path to the most optimal goal paths in relation to a specific product.

With these parameters, the algorithm calculates the difference to arrive at the next closest pattern leading to the goal. As the user interacts, the algorithm appends the new action and re-adjusts to output the closest probable.

purchasey

The working of the algorithm can be visualized as per the diagram above, the pattern of user interactions is mapped from the initial event to the desired goal path. After analyzing a particular user’s interaction flow for a particular product, it can arrive at the most effective conversion pattern for a single product.

The area highlighted shows the most probable purchase paths for the given product, which is used to verify against with the current browsing pattern of the user. Now whenever the user’s pattern heads in a certain direction, the algorithm for matching patterns can quickly locate area coordinates and return the nearest event node to deduce possible pattern extensions.

These patterns are stored as strings and on every interaction, the string is updated which contains a list of associated products along with the association score, which gets updated after each relevant interaction, all in real-time.

This scoring is done after every interaction so that the updated score is available for recommendation at all times. The recommender outputs a string ([X101, P001{(P003, 0.966), (P004, 0.877), (P007, 0.746), (P002, 0.335), (P009, 0.200)}, where X101 is the user id, P001 is the product (product ID) and the product ids within the bracket are associated products with corresponding weights.

To ensure finer supervision, nested rules can be configured. For example, you can add a rule to exclude a product from the recommendation list if the product is already purchased by the user or exclude a product from the recommendation list if the user has an unresolved issue. This would allow marketers to fine tune their recommendation intelligence and make machine driven personalization more human.