PEGA Customer Decision Hub - One-to-one customer engagement - Module Quiz and Answers

The Pega Certified Decisioning Consultant (PCDC) certification is for professionals participating in the design and development of a Pega Customer Decision Hub™ solution. This certification ensures you have the skills to apply design principles of Next-Best-Action Designer, Decision Strategies, and Predictive Analytics.

 PEGA Customer Decision Hub - One-to-one customer engagement - Module Quiz and Answers

MyCo, a telecommunications company, wants to implement one-to-one customer engagement using Pega Customer Decision Hub™. Which of the following real-time channels can the company use to present Next-Best-Actions? (Choose Three)

·             The call center

In a call-center, a customer service representative can use Next-Best-Action recommendations to provide one-to-one customer engagement.

·             SMS

SMS is a valid channel.

·             A retail store

In a retail store, a customer service representative can use Next-Best-Action recommendations to provide one-to-one customer engagement.

·             Billboard on the company building

·             Traditional television advertisements

 

Which statement best describes the goal of Next-Best-Action?

·             Ensure that the customer is always given a desirable offer.

·             Balance customer needs with business objectives.

Next-Best-Action weighs customer needs against business objectives to optimize decisions based on priorities set by the business. This is the goal of Next-Best-Action.

·             Ensure that every customer receives the same action.

·             Provide insight into business processes.

 

Next-Best-Action ensures that communication between the business and the customer is__________ and __________. (Choose Two)

·             free of jargon

·             uniform and generic

·             timely

The artificial intelligence embedded in the Next-Best-Action platform ensures that every message is relevant and timely, so the customer can take immediate action.

·             contextual

The artificial intelligence embedded in the Next-Best-Action platform ensures that every message is relevant and contextual, so the customer is more likely to respond.

PEGA Simulation - Test and Learn Approach to Maximize the Marketing KPIs

What is ‘test and learn’ marketing?

Test and learn marketing is a data-driven approach to constantly optimizing Strategies and to improve the KPIs performance of recommendations made from PEGA Decisioning Simulation capability through a series of ongoing tests. It allows organizations to convert insights into hypotheses and then test those theories to prove their value. Primarily to improve the take rate of recommendations made from PEGA OR it could be improving the revenue AND/OR margin for an Organization. It also could be a two different variant of the Treatment for the same action to the same channel. Eg. Having two variants of the Email treatments for the same Action. This is a perfect example of test and learn marketing.

Test: Taking the guesswork out of marketing

Test and learn principles remove this speculation by building a data-driven system that proves the success and failure of individual marketing strategies. Instead of relying on correlative insights, we can run the PEGA Simulation to attribute meaningful KPIs to each change, measure performance and test variations to determine which strategy is most effective.

Learn: Turn insights into better marketing decisions

The learn aspect can be as simple as finding out which hypotheses are correct and prioritizing them in order of value (which meets the Organisation KPIs objective already set). We can also learn from previous experiments (and their data) to create new hypotheses or determine which testing opportunities meeting the marketing KPIs.

We could also leverage an existing PEGA Decisioning Machine learning AI capabilities like Adaptive Modelling, Predictive Modelling to experiment to spot opportunities for testing, makes recommendations based on your results and predicts the outcome/value.

A good test and learn marketing principles has two main objectives:

1. Increase conversion rates through testing variations.

2. Learn from previous tests and apply findings to future marketing decisions.

We run a series of tests to determine the best outcome for specific scenarios and we collect all of this data to spot patterns and inform smarter marketing decisions.

By applying a test and learn model to all of our marketing campaigns, we can improve every aspect of brand performance with data-driven insights. The more tests you run and the more data we collect, the more reliable these insights become and the more robust our marketing decisions will be.

Some of the example of Goal for Test-and-Learn approach

• Measure the Impact of Decisioning Framework values (Real Time Controls) changes and to align Organizational KPIs

• Test the effectiveness of different treatments for the same Action for the same Channel Eg: Keep different Email Treatment (Title and/or content) for Control and Test to measure the impact

• Determine which Treatment Content generate the highest engagement.

• Test the potential value of a new Channel.

• Calculate the lifetime customer value of leads based on acquisition channel and their position along your marketing funnel.

• Identify negative friction that’s preventing leads from converting into customers.

• Optimize marketing funnel to reduce dropouts and maximize conversions.

• Optimize the customer experience to maximize repeat purchases and lifetime customer value.

A test and learn model can be applied to any marketing objective and campaign type where we want to maximize performance and collect insights to inform better marketing decisions in the future.

How to apply a test and learn model in PEGA Decisioning & Marketing

Some quick overview of the key steps to take in test and learn model are:

1. Define goal and KPIs: It’s crucial that Organisation should have a specific goal in mind and understand which KPIs truly measure success. It could be higher engagement reate, higher order rate, improving revenue, margin and reducing churn etc.

2. Develop hypothesis: This is the theory that your tests will prove or disprove – to achieve the same KPI, there could be different school of thoughts. Ideas will help us to develop hypothesis.

3. Create testing criteria: To get meaningful results from tests, weneed to define strict testing criteria and remove every possible variable that might skew the desired outcome.

4. Test ideas with the highest potential first: This will have the biggest impact on performance and collect the most valuable data early on – but it also ensures that our test and learn models achieve a positive ROI as soon as possible.

5. Run your tests until statistical significance: This ensures your results are reliable enough to inform decisions while mitigating for variables outside of your control.

6. Turn insights into action: Individual results help you make specific decisions but collecting data from all of your tests helps you to make informed predictions and decisions for future campaigns – for example, which ad format is most effective for your campaign message or how much to bid to achieve your target reach.

7. Measurable success post implementation: Once right Strategy has been identified, implement the hypothesis produced results closed to KPIs and make the BI tools in place to measure the success/failure of the recent change.

8. Apply Continuous Improvement Technique: By learning from successes and failures, we continue to do what works and constantly improve upon what doesn’t

This test and learn approach can be applied to any marketing strategy and, PEGA Customer Decision Hub Simulation capability would help to achieve the possibilities in a quick turn-around time.

Happy Learning!

Nanjundan Chinnasamy

PEGA Simulation - A Technique in the PEGA Marketing to test and Solve Problems in the Marketing KPIs

Today i would like to give some insights on The Use of PEGA Simulation in Solving the Problems in Business/Marketing KPIs. This article focuses on utilization of analysis of different possibilities using a Simulation feature made available in PEGA Customer Decisioning Hub. For a given business problem, there will be more than one solution. Each solution is analyzed with the help of simulation. They have been evaluated and the optimal solution was selected. 

The modern trend these days is to optimize everything that we can, especially in Marketing when PEGA Customer Decision Hub installed to drive the Marketing messages for an Enterprise. The result is the achievement of predetermined targets (KPIs), such as reducing Churn, Increase Revenue from Each customer, Increase Margin and increase the proposition value for each customer. Most of the Organizations are lagging in measuring & optimizing the Outcome of the Marketing messages against Organization KPIs. It is not all about recommending the ‘number’ of marketing messages at various customer touch points. Organizations could start exploring the ways to optimize the current logic and run the various simulation options to understand the Strategy which brings values close to the KPIs made available. 

Let's discuss one of the Telecom business problems and understand how PEGA Simulation will help Markets to test and Learn to maximize the business outcome. Aka. KPI

Problem:

Let's consider three huge companies in the telecommunications market in the US. Each company sold some of the four products (known as “quadruple play” when combined), including high speed Internet, cable TV, telephone, and cell phone services. Many households purchased individual services from the different companies. The aggressiveness of the players made “product bundling” a key marketing strategy, but there was not a clear understanding of how to use this strategy, or the impact it might have on the market.

Company A wanted to enter the markets that they hadn’t covered yet OR they wanted to cross -sell other products, but each one knew that penetrating the competitor’s niche would force them to reply in the same manner. 

The challenge was to build a Strategy (Including Product Price and Offer price etc.. ) that would allow the client to analyze several scenarios, taking into account the whole telecom market (with its 3 major players) and the “product bundling” effect. 

Solution:

After doing enough market study, the Marketing Team of Telecom company Arrived prices and offers for each product bundle combination. Marketing team arrived at the conclusion of “quadruple play” buble cost could be either $200 OR $220. They are not sure which price point would help to benefit both the customer and Organization to maximize their value.  

Organization A can leverage PEGA Simulation capability to understand the impact of business KPIs (Churn, Revenue and Margin) keeping the possible price combinations to determine the best pricing Strategy. Strategy which gives us closest KPI outcome can be selected for Implementation. 

Outcome:

It was the high flexibility of using PEGA Simulation capability to build, measure and Learn possible what-if scenarios. Outcome of the Simulation result can be discussed with Marketers explaining various organization specific KPIs. Marketing team could make Informed Decisions based on the simulation results produced for each what-if variance.  

Simulation is one great technique that allows us to have a reliable view of possibilities before getting into Production. So we have a simple way to test an infinite number of variants to solve the problem and for the actual implementation to choose the best possible solution to the situation.Simulations help to detect problems and complications that resulted in normal operation and save significant costs that we had to then spend on their removal. In practice it is impossible to try different alternatives, in terms of irreversible steps that are expensive. 

 

Conclusion:

By using simulation experiments, we can obtain results for different alternatives. Team can share the informed choices to the Marketing team before taking Decision to implement any Marking Strategies. There is a detailed knowledge base article made available in PEGA for managing Simulation tests in Customer Decisioning Hub. More details can be found here https://community.pega.com/knowledgebase/articles/decision-management/85/managing-simulation-tests-pega-customer-decision-hub

Drive Marketing Growth with the help of Real-Time Speech Analytics combining Al powered PEGA Decisioning

Creating a positive customer experience is the key to building a successful business. Knowing what customers want before they change their minds, or move on to your competitors, will help create a positive customer experience. Since you can't read their minds, the next best thing is using speech analytics to capture 100% of your customer conversations and combine the Speech Analytics in PEGA's Next Best Action Decisioning Engine.

Customer feedback gives us the ability to better understand our customers and their concerns, needs, and ideas. Speech analytics saves businesses time and delivers value through loyal customers, increased revenue, and decreased expenses. But in reality, combining Call center Real-Time speech Analytics with the PEGA Decisioning Engine is not a straightforward and easy process. With the recent Rega Acquisition of Qurious.io (for Al-Powered Speech Analytics), integration of Speech Analytics in a PEGA Decision making process would become easy (hoping to hear updates from PEGA world 2021 event on 04-May-2021).

Large Enterprises contact centers typically address multiple challenges and try to resolve them through a measurable improvement process. In simple words...

PEGA Speech Analytics + PEGA NLP+PEGA AI powered Decisioning = Solve many industry problems exist today

I strongly believe Speech Analytics with the power of AI powered Decisioning would solve many of UNCOVERED industry issues today. Here are the most common areas that require constant business improvement.

Improve agent performance and reduce churn

Speech analytics can listen to every call. By listening for keywords and phrases, ensuring the agent follows processes, and by analyzing both agents and customers language and tone for sentiment, every call can be automatically scored against multiple criteria. What's more, these rules will be applied perfectly consistently. As a result, you can improve overall performance with additional training to plug the skills and knowledge gaps identified. This also helps to improve the speed to competency for new recruits, and reduce chum in those all-important first few weeks and months.

Speech analytics can also help us to stay in touch with the emotional and mental well-being of our agents Tone and sentiment analysis is used to assess both parties in an interaction If an agent shows signs of stress, frustration, anger or other negative emotions, this will inevitably come through in the analysis Managers can then be warned to intervene with a metaphorical arm around the shoulder. additional training, or even counselling.

Gain insights into customers and competitors

Even if we are already recording all calls and listening in to a small percentage of them for compliance and training purposes, that still means that most of the useful information in those interactions is not being taken on board.

While talking to our agents, our customers give up lots of incredibly detailed information in off-the-cuff comments related to such things as how they use our products, how our prices compare, how accessible and efficient our after-sales care is, and most importantly how we stack up against your direct competition. This is gold dust insight that you would normally need to conduct thousands of surveys to uncover and it's right there already on a dusty server in your unlistened to call recordings.

Sales and marketing teams can leverage this information with the help of modern technologies and use it to inform their decisions on everything from product development to pricing, to market positioning and what sales channels to use.

Generate more sales

Real-Time Speech analytics combined with Decisioning allows us to identify what sentiments in what situations lead to what behaviors and what the results usually are.

We can then redesign processes to eliminate behaviors that don't lead to sales and retention, and encourage ones that do Decisioning logic to take advantage of the best moments during interactions to make an upsell or cross-sell offer, ensuring that closing rates are maximized Outbound contact.

programmers can also be used to follow up with customers who had, for whom the analytics predict are likely to have a negative experience with a product or service.

Predict and understand your metrics and improve them

Using speech analytics, we can take the measure of each customer's tone of voice and words used to actually predict- quite accurately the customer satisfaction or NPS rating the customer would have given for a particular call This means that rather than relying on the few customers we do follow up with to arrive at those scores, we can effectively get a predicted satisfaction rating for every interaction.

Linguistic analysis can also pick out customers' verbatim comments made to agents to help us understand exactly what they do and do not like about our products, service levels, and processes Feed this information into our analysis of our metrics when you collect them to understand why customers give the scores they do, and what they would like us to do to improve them.

Prevent Customer fraud

Customers may be "king" but that doesn't mean they always do the right thing Speech analytics can not only identify but predict the likelihood of customer fraud with the help of Al Using the outputs of speech analytics (transcriptions, interaction analytics etc), a supervised machine learning module was built against a training data set ("Yes Fraud" and "No Non-Fraud calls). After blind tests and ongoing improvements, the supervised learning model has been operationalized as a customer voice data feed to the existing Fraud Risk engine. Customers are now triaged based on enhanced risk prediction to our Marketing Decisioning Engine

Turning Insights into Actionable Results

Speech analytics can undoubtedly provide its users with a wealth of agent, customer and contact center related information. However, the real trick lies in finding the most relevant insights from the pool of agent-customer interaction data. These insights can then be used to attain the required results and keep the contact center's operations away from any trouble Speech Analytics Insights are "Gold Digger" for business team and use them to make actionable results in both inbound and rest of the customer touch points including outbound channels.

Understanding the Factors Causing Call Dispositioning

Commonly used to describe the outcome or final output of a particular call, call disposition codes or labels are used by agents to classify calls under different heads such as miscellaneous, need follow-up, left voicemail and others Agents can attach such tags to these calls due to several reasons such as impractical call, need monitoring by the agent, saving time and others.

With speech analytics, it gets easier to classify the available call data under different heads so that it gets easier to attain the desired results. It would also get easier to know the reason behind a particular call, gain relevant business insights, etc.

Identify and correct processes or automation outliers

Organizations can use speech analytics to find terms associated with specific processes. Organizations can use Speech Analytics to understand the workflow/Processes broken/requires betterment to improve customer experience.

• Fulfillment of an order

• Refunds or returns

• Employee automation Missing items

• Broken systems

Average handle time (AHT) reduction

Understanding long calls also helps identify what customers really want and what the pain points in our product, services are and uncover the potentials to improve both Agent and customer experience.

Resolving customer issues on the first call (Improve first contact resolution)

First call resolution, also known as first contact resolution or FCR, is a company's ability to handle a customer's call, email, question, or complaint during their first outreach for that specific incident. With the help of Speech Analytics in a Decision making process will help us to resolve customer issues on the first call.

What do you think? Do you think we can solve other usecases? Please share in comments section.

Happy Learning,

Nanjundan Chinnasamy

Al Powered Real-Time Speech Analytics in PEGA Decisioning & Marketing My expectation for PEGAWorld 2021

With the recent Pega Acquisition Qurious io (for Al-Powered Speech Analytics), the dynamics of Call Center solution in CRM would be changed in coming days Both Inbound & outbound service calls can be analyzed in real time. Qurious io's speech to text conversion capabilities, Natural Language Processing (NLP), and emotion detection capabilities to analyze the dialog within each customer service call real-time insights can be feed to PEGA's Centralized Decisioning Brain to improve customer interactions, make better recommendations, and boost customer loyalty and sales. I am expecting a major announcement on this topic in PEGAWorld 2021 planned on 04-May-2021.

Major clients adopted PEGA CDH solution as Marketing tool and exposed their Decisioning logic as a Service to integrate with Organizations Proprietary legacy complex call center software to service customers. There is an Increasing demand for combining Real Time speech Analysis In-sight with PEGA's Decisioning Engine to solve many known business problems exist today in Call-Center application Call Center is a very good channel to connect with customers, understanding the feedback for our products and services and most importantly the customer concerns, emotions and feedback (both verbal and non-verbal sentiments) etc.

Improve Strategic Business Decisions

Improve strategic decisions basis sentiment and conversation insights to understand your customers better to refine your future serviceability and product offerings

Manage Operations Efficiency

Basis the conversation analytics reports analyze your team's performance in handling the conversations and design future training courses for Call Center Agents. The outcome will be smooth chent handling benefits while offering improved Customer Experience.

Accuracy

Make more sense of transcription with domain specific targeted words/phrases likes addresses, currencies, years, abbreviations etc.

Couple of Call Center usecases we can solve with the help of Al Powered Real-Time Speech Analytics in PEGA Decisioning & Marketing Contact center solution to improve the customer experience by providing real-time update about the offers, services.

Keyword Spotting:

Focus & identify specific Keywords and parses to filter only the relevant conversations derive domain specific insights & performance Speech Analytics keyword can be used in Decisioning Engine to pitch Sales/Service specific messages in Real-time.

Theme Discovery

Identity the Intent & purpose of the conversation to take proactive actions and manage them better.

Customer Segment Identification

Create & identify the new customer segments based on their conversations to enhance the personalized experience.

Root Cause Analysis

Once PEGA NLP categorized the calls using Real-Time Speech Analytics, PEGA Decisioning capability can be utilized to make Product recommendations. Result of the Decisioning outcome can be used to device the root cause combining NLP results. It will help us to find out what you do not know to look for.

Reps' Conversation Analysis

Analyze Reps conversational etiquette like talk to listen Ratio and speech ratio to plan the Rep's personalized training Analyze every conversation and identity the trends or insights per Agent/Employee.

Real Time Alerts

Trigger Real-Time notification on the occurrence of custom words or events important to an Organization. Primarily to Save the customers when the customer identified as High-Risk customers and the likelihood of churning is High.

Soon I will be publishing detailed usecases and the benefits organization can unlock with the help of Speech Analytics in PEGA Decisioning. Stay tuned!

Happy Learning,

Nanjundan Chinnasamy

Enhance Your Personalized Messages to Customer using External Data Feeds/Public data

Mostly Organizations are using their own Customer Analytical Records (All the information about a Customer data, products, usage and Marketing data) as part of Decisioning process whether its Inbound channel Real-Time request or Proactive outbound communication to send the personalized marketing message using tools like PEGA CDH, Adobe, SAS, Unica etc.

Personalization has become a must-have for messaging because of high customer expectations. To take Marketing personalization to next level, integration of external data feeds/publicly known information about the customer as part of Decision making process is inevitable.

I would like share few use cases which can enhance personalized messages to Customer in the Communications and Banking industries.

External Data Feed#1: Integrate Official Emergency services & weather alert systems API data to PEGA Decisioning engine

Using weather alert system data & joining the same with Organizations own proprietary Customer Analytical Records will help Organizations to target the Customers affected by bad weather/ natural disaster. (Post code can be used to identify the customers impacted to send hyper personalized marketing message). External data source will help Organization to prioritize the Nurture/Service messages to the Customers over Sales message.

Corporates can also use the publicly available weather alert systems API Data & send proactive service/alerting messages. Timely, relevant and Empathetic message will help Organization to increase customer Life Time Value and NPS.

Sample use cases to use the External source data to send hyper personalized Marketing message:

When customer postcode matches with natural disaster affected postcode:

o    For Banking & Communication, Waive-off late fee & allow them to pay the bit later

o    For Communication, Make some additional data & call free offer for affected customers until the situation is restored

o    For Communication, Send proactive service outage messages & notify the team who are managing the Infrastructure/Towers about the event.

o    For Banking & Communication, Prioritize the Nurture messages over sales (send proactive service outage message with ETA to restore the service)

o    For Travel, send a message with weather information for an upcoming trip

o    For Retail, send users a list of products that they might be interested in based on the current location (if its affected by Bad weather)

External Data Feed#2: Integrate Foreign Exchange API data to PEGA Decisioning engine

For the customers who are buying the ForEx regularly OR the Customers who are setup the Standing order to other Country, proactive personalized offer when there is a significant change to the ForEx will help Customers to book the ForEx in advance.  Hyper personalized Marketing message will help Banks to increase their ForEx sales and Customers would happy with the Exchange Rate deal they received.

To talk specific to PEGA Decisioning, mostly the prioritization of the offers/Actions is combination of ADM Propensity, Context Lever, Offer Value, Business Lever etc. Managing Offer value, Business is done by the Marketing Department most of the time through Revision Management process AND/OR other well defined Rapid deployment feature. Majority of the time, the opportunity will be lost before marketing team reacting to get the Marketing lever changes validated and implemented in Production. Very few Organizations doing Rapid deployment in 2 days’ time, few are doing Revision Management deployment in 2 weeks and majority of them deploying Marketing lever changes in 3-4 weeks’ time (BAU release change release).

Integration of External source data source could also help us to override Marketing levers to specific Target Group (Customers affected by bad weather in my previous example) and also it will help us achieve automated data-driven decisions from PEGA in real-time. This will eliminate of manual lever changes to force the specific/group of propositions to the customers who are in-need.

What are the other use cases you can think about to make the hyper personalized marketing messages by integrating External data as part of Marketing Decisioning process? Please share those in comments section. 

Happy Learning,

Nanjundan Chinnasamy

How does PEGA Marketing/Decisioning Strategy work? Understanding the PEGA Marketing/Decisioning Strategy execution through “Marketing funnel”

I have come across the scenarios where Marking team is finding difficulties in understanding how does PEGA Marketing/Decisioning Strategy work. As a technical guy, I found bit difficult in explaining the PEGA’s complex Decisioning Strategy logic in non-technical terms where all Markers can understand the complex logic.

I would like to explain the PEGA Strategy logic with the “Marketing Funnel”. Yes, PEGA Strategy rules filters the offers using Marketing Funnel technique. All The eligible offers will send over to the top level strategy and each strategy has logic to filter the offers (applying rules) similar to the Marketing funnel. PEGA Strategy has many pre-built Strategy components & uses the same to select offers and send to next stage.

A Marketing funnel is a way for us to track the Offer eligibility of customers. By understanding where in the funnel, or where in the customer’s eligibility, Marketers can decide what kind of marketing message to send them.

The marketing funnel is simply a visual representation of a sometimes difficult subject to visualize. Think of it as a map of your businesses marketing and sales processes, procedures and strategies.  Left of the funnel includes your broadest marketing efforts and is narrowed as potential Offers move through it. The breadth the funnel goes the more focused your potential Offers become. Eventually you will weed out all the Offers that are not willing to recommendations and left with the most valuable sales qualified Offers. 

Marketing funnels are so effective is because it makes you take a hard look at exactly how our business recommendations various offers based on the context. What eligibility criteria to adjust to recommended the offer to balance customer needs & business objective.

Defining Engagement Policies:

Defining Engagement policies in PEGA7 done by when rules & set of PropositionFilter. All the criteria's are classified as Eligibility/Hard rules.  

Defining Engagement policies in PEGA8 simplified with NBA Designer. PEGA8 NBA Designer uses E(Eligibility), A(Applicability) and S(Suitability)– Rules by clearly separating engagements.  

• Eligibility rules, which are strict rules representing what it is possible to offer.
• Applicability rules, which represent business practices that limit what to offer based on a customer’s current situation. These are not as strict as eligibility rules.
• Suitability rules, which represent what the business should and should not do ethically and empathetically. These are the least strict rules.

Pega Decision Management combines business rules (Engagement policies- specify the conditions under which an offer or group of Offers is available for a customer), predictive and adaptive analytics with real-time Decisioning to initiate and adjust processes based on the business criteria we have established. Inbuilt PEGA Decisioning rules help us to select the right offer ensuring the right decision for the right customer at the right time.

Pega Decision Management makes defining complex decision strategies simple and efficient to help organizations:

• Dramatically improve customer experience in every channel with strategies based on real-time, actionable insight into operational and behavioral data.
• Rapidly deploy intelligent processes with pre-built business accelerators to create and maintain comprehensive decision strategies without the need for any coding.
• Make dynamic changes to decisions, processes and interactions based on real-time customer data and feedback.
• Leverage adaptive and predictive capabilities to make the best possible decisions based on historical and real-time responses.
• Seamlessly integrate existing applications and processes to improve operational efficiencies and respond to customers across all channels.