Monthly Archives: December 2021

AI Driven Marketing

AI driven Marketing has announced its arrival, but privacy concerns prevail

S. Ernest Paul

With Cookies going away, and Apple hosting pixels which reveal open rates for emails for Marketing Automation there are concerns – driven by privacy spurred by GDPR and cookie consent – the DMPs are dead. With the latest iOS 15 update the IP addresses which revealed location data also took a nose drive

First party data which brands hold in data warehouses and data lakes are akin to the 11 finger lakes in Upstate NY.

Disparate, with no data governance platforms / frameworks / normalized data or elastic data is still nascent at many brands, with no sign of a Chief Data Officer, albeit staffed with a good sized platoon of data scientists.

The Data ship needs a captain, and the lakes need to be bridged & governed

Fig: The Disparate State Of Data

Few years ago, at the advent of Social listening, the term social selling kicked in and at a Payor a tweet ‘Hello, I am turning 26 on and no longer going to be on my Mom’s insurance’ was a melody.

Just this nugget, a trigger to the Healthcare marketer was exciting. However now with RPA incorporated into social listening intelligence these finds are self-driven.

When I was at Cigna and set up their Social media practice from scratch, the key piece from an intelligence perspective was ‘social listening’. I did set up the Social listening listening along with the publishing and the front line conversations.

Except today, this ’nugget’ would be picked up by AI without any human intervention

Real Time Marketing needs to get Real

Real-time marketing capabilities do emphasize the actionability of marketing capabilities, however the entire chain is not strong enough yet allowing marketers to actively manage their marketing activities and track marketing capability development for products and services improvement, relying on AI -driven insights.

The same Real-time marketing capabilities also help marketers gain situational awareness for their marketing actions, which allow marketers to focus on each customer conversation that matters most to customers with real-time monitoring and big data analytics. By doing so, marketers can make the right decision at the right time

The Buyer’s Journey, The Sales Funnel – allow visitors to evaluate a product & formulate a decisioning rationale to make a purchase and then continue as customers and exhibit possible loyalty. See Fig 1, a couple of paragraphs below.

Marketing Goals have not changed but the goal posts have

The goals for the business and marketers is to generate more conversions (which primarily consists of sales). They deploy various marketing tactics, Marketing automation – segment the population set and send personalized communications via eMail/ SMS etc. They supplement their marketing efforts (MQLs) Marketing qualified leads and then filter it down to (SQLs) for the sales teams to follow up. The brand communication support the effort via various tactics and channels from SEO/SEM/ Targeted Advertising, Social media, Retargeting, Conversion rate optimization, and other methods.

Internal Data clean-up for AI – please can we do this first?

Lately, many brands have been able to clean their internal data, removed and eliminated disparate data warehouses and data lakes, made it possible for the data to be housed and queried. The data science teams have worked hard to make this happen as a result AI has been able to isolate, pick-out customers and gently nudge customers towards marketing and sales cycle, and improving the conversion rates and keeping the cost of acquisition (CAC) within manageable budgets.

Customer Loyalty and Retention reflects the LTV (Long Term-Value

S. Ernest Paul

Fig1: Sales Funnel / Buyers’ Journey / Customers Lifecycle

If the Retention is not holding and the data is elusive customers are fickle they will leave for a competitor, especially now when the COVID tension is high and attention spans are shrinking. We really need ‘Nudge’ to sharpen the experiences and personalize the journeys or we will end up with a Chinese Menu with a million items to choose from – a maze. Now compare that to ta Starbucks menu (Nudge-inspired). Thank you, Prof Thaler @Kellogg for Nudge economics.

Fig2: Customers leaving for a competitor

A New Alliance is being Stitched – CIO & CMO

To stop this leakage and to have the ability to identify, predict and react in near real time a new team/ alliance from within has been born. The key decision makers in defining the Long-Term AI strategy are the CIO and the CMO. Lately the role of the CDO (Chief Data Officer) has taken shape as the origins of all AI driven activity points to the glaciers of data.

Fig3: The stakeholders involved in shaping and championing Data, Machine Learning, AI

The technologies involved before AI kicks-in

S. Ernest Paul

How do artificial intelligence, machine learning, neural networks, and deep learning relate?

Perhaps the easiest way to think about artificial intelligence, machine learning, neural networks, and deep learning is to think of them like Russian nesting dolls. Each is essentially a component of the prior term.

That is, machine learning is a subfield of artificial intelligence. Deep learning is a subfield of machine learning, and neural networks make up the backbone of deep learning algorithms. In fact, it is the number of node layers, or depth, of neural networks that distinguishes a single neural network from a deep learning algorithm, which must have more than three.

The nodes at work below are illustrative

If Retention is not sticky and the experience is elusive, the customers are fickle and will leave for a competitor unless of course you are locked into a Xfinity / Comcast deal from which there is no escape for they only tailor to ‘acquisition’ (new customers). I , along with many of us are in the ‘retention’ phase – we get little attention & deals – of course specific to the industry with complete disregard to the sanctity of the ‘Sherman Act’ – my apologies I went on a tangent there).

Data still remains a nemesis

S. Ernest Paul

My creation below illustrates the phases and advances towards AI – however data remains a nemesis. Without it, Marketing and Sales teams are blinded. The Marketers still use Marketing Automation platforms & Lead Generation tools crunch MQLs (Marketing Qualified Leads) with segmentation / identity / personas / personalization – however the ROI on the the MQLs translating to ( SQLs) Sales Qualified Leads is shabby.

AI can only deliver if the data is reliable

Awareness > Consideration > Decision > Loyalty

The No. 1 goal for most businesses is to generate more conversions (which primarily consists of sales). This can be through their marketing efforts, sales tactics, brand communication, conversion rate optimization, and other methods. Of late, many companies have developed critical competencies in using AI to nudge customers towards sales, and have improved their numbers drastically as a result.

The customers are increasing puzzled with the COVID phenomenon and there is mad scramble to get UX / Design thinking and User-Centric design right.

AI, machine learning, and big data technology can all work hand-in-hand to improve the customer experience and support an optimized customer journey, which leads to more conversions in several key ways.

Let’s talk about how you can start using AI tech in each stage of the funnel.

Awareness

Marketing strategies these days are often heavily focused on the top of the funnel to build brand awareness and attract new customers. For many businesses, recognition is nearly equivalent to the value of their brand. Elena Veselinova and Marija Gogova Samonikov explain in their book Building Brand Equity and Consumer Trust Through Radical Transparency Practices that brand impact is a continuous process that insures purchases, cash flow, revenue and share value. Brand communication and experience creates and builds a loyal base of customers that do not consider any other brand.

Brand Awareness

Creating a strong level of brand awareness takes time and strategy. Companies spend millions of dollars on marketing campaigns and advertising to increase their reach and recognition, but AI tech is able to take the guesswork out of these strategies by analyzing huge volumes of consumer data for more targeted campaigns. For example, predictive analytics software can collect, track, and analyze datasets from past customers to determine which strategies or tactics performed well. These datasets are turned into reports with insights to guide marketing efforts and place relevant content in front of the most interested eyes at the right times.

With AI-assisted marketing, advertising strategies can be backed with data to optimize ad placement. Machine learning systems can even identify the best influencers for brands to partner with in order to reach relevant audiences and grow brand familiarity.

Consideration

The next step of the buyer’s journey is often overlooked by marketers because it can drag on for a long time, depending on the product and the customer’s needs. During the consideration phase, a customer is already familiar with a brand or product but are unsure of whether or not to actually purchase. Customers will typically research the product’s reviews, compare prices to competitors, and look for alternatives during this stage. Due to this, the number of potential customers tends to narrow down considerably as they move from this step to the decision phase.

Brands must work to combat each customer’s concerns and questions standing in the way of a purchase decision. One of the best ways to do this is by offering personalized content that is relevant to each person, making it easy for them to find the information they are seeking.

AI systems can be used to predict a customer’s needs based on consumer data and previous online behavior, and then encourage conversions with a tailored UX or even a completely customized landing page that displays content relevant to that customer.

For example, if a site visitor has viewed a certain product page and played a video demonstrating its features, these actions can trigger an AI system to target them with personalized content that prompts a conversion if they don’t proceed to buy immediately. This content could be something as simple as an email message with more information or a display ad with a special offer for the specific product.

Then there are platforms that use conversational AI tech (such as chatbots and voice assistants) to power automated, text- or audio-based interactions between a business and its customers. These platforms can understand speech, decipher intent, differentiate between languages, and mimic human conversations with great accuracy. Increasingly, they are advanced enough to even understand individual context and personalize the conversation accordingly.

Data Insights

Based on data insights, AI tech can curate content that matches up with the issues that are most important to that person, whether it be product features, immediate delivery, long term savings, etc. Customers respond quite well to personalized offers — an Accenture study reported that 91% of consumers are more likely to purchase from a company that sent them targeted deals or recommendations.

Decision Making

Once a customer moves from consideration to action, AI tools can be used to support a positive sales experience and eliminate any bumps along the way. If a customer encounters an issue while browsing the site, or during checkout or payment, it could be an instant sales killer, if it isn’t handled immediately by something like live chat.

A challenging consideration towards a design response is certainly necessary.

According to multiple studies, one of the most frustrating parts about online customer service is long wait times. By using AI-enabled chatbots, companies can instantly answer common questions and resolve issues or roadblocks affecting the progression of the buyer’s journey. And customers certainly appreciate these quick response times. AI systems can significantly increase conversions with effective personalization and swift customer service.

Loyalty & LTV

The last step of the customer journey is possibly the most valuable. Over half of customers reportedly stay loyal to brands that “get them.” Returning customers also tend to spend more money than new ones, and an oft-reported stat says that on average 65% of businesses’ revenue comes from existing customers.

What can the Business do

Businesses (and customers) can benefit greatly from loyalty programs that are backed with machine learning technology. Starbucks famously uses AI tech to analyze customer behavior, improve convenience, and identify which promotions would perform best based on that person’s drink or food preferences, location, and purchase frequency. Their loyalty program uses this data to send out thousands of offers each day for the products their customers are most likely to buy. Their customer loyalty program grew 16% YoY last year as a direct result of their Deep Brew AI engine.

While a positive shopping experience and great products are certainly important factors in a customer’s decision to buy again, data-driven marketing campaigns that encourage loyalty can also help a company to grow their numbers of repeat sales. Again, AI-assisted personalization techniques can boost the chances of a customer coming back for more, especially if they receive targeted offers or shopping suggestions based on previous interactions.

The Wrap

AI is proving to be the tool of the future for marketers. It allows marketing teams to use predictive insights and analytical data to encourage and assist every micro-decision taken by consumers. AI systems not only help customer

Who is S. Ernest Paul ?

Notable – He was recently recognized in the Top 10 CMO in the Country in 2021

b) He was recognized in the top 100 in Finance in 2021

Finance Magazine | C Level Focus

A thought leader in Data, Digital Strategy, Social Media, Content Platforms , Content Strategy, Digital Marketing, Adtech, Access Management, Identity, Adtech, Search engine Optimization, ML, Neural Networks, AI, and a Marketing Technology editor at Digitalbrine.com and author.

He is also a staff writer at Medium for ‘Data Driven Investor’ and other publications.

Robotic Process Automation is a win-win

S. Ernest Paul

Robotic Process Automation has delivered and continues to deliver a very healthy ROI for brands Strong business leaders who are looking beyond cost reduction are leveraging RPA as part of the Digital transformation effort – freeing up valued human capital and realigning them to new tasks with the highest business value, which often enables new consumer-facing business models.

My research has shown RPA is the largest recipient of healthcare budgetary allocation for this year and counting, in the emerging technology class.

S. Ernest Paul

Let’s face it – Healthcare organizations accumulate patient data at a rapid pace daily. With affordable Cloud storage availability, the rapid emergence of new technology, processing speed, tools, software, and a soon 5G speed implementation, RPA is a prime candidate in healthcare to gain on process efficiencies providing a partial solution for a larger Digital Transformation effort within Pharma, Life Sciences, Hospitals and Health Systems

Gains, efficiencies and consumer trust can be achieved from RPA initiatives resulting in budgetary shifts with employees aligned to focus on critical digital customer-facing initiatives firmly placing the customer at the center.

Key forward thinking digital hires, customer facing digital properties and key marketing technology personalization initiatives have to provide a concerted and impactful effort to plug customer retention leakage and reignite LTV

Fresh digital and omnichannel engagement initiatives with redirected capital from RPA driven savings just makes sense. The NPS scores and lower acquisition rates need immediate attention. RPA initiatives running parallel provides this assuring equilibrium.

Legislative background

With the passage of the Affordable Care Act in 2010 and The Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009 was an ambitious policy effort to increase the adoption of electronic health records (EHRs).

THE HITECH Act was enacted, prompted by evidence that the use of EHRs could substantially improve the quality and efficiency of care delivered. We are now soon heading into 2020 and the good news is that adults with health insurance is +20 million since 2010 according to the National Center for Health Statistics.

The current Financial health of Hospitals and Health systems

This improved access to healthcare juxtaposed with an aging baby boomer population with increased healthcare needs and medical care has burdened hospitals and health systems financially. Data suggests greater than 50% of hospital and health systems revenue is from their market investments than from their core business of healthcare, a troubling sign. According to Deloitte, between 51 and 60% of hospitals could see negative margins by 2025 if they are unable to achieve productivity targets.

What are Health systems, hospitals, and others doing to get ahead of the curve

There are multiple concerted efforts by Pharma, Life sciences, Health systems and Hospitals to harness data to spawn new ventures, explore and partner with adjacent ecosystems. Some are seeking to consolidate with other health systems and hospitals and leverage economies of scale.

The one standout knight in the playbook is RPA, traditionally outsourced can now be brought in-house or used as RPA as a Service from Beyondiris Consulting.

Key Area of Opportunity is Productivity utilizing RPA

RPA in its nascent form are software programs or ‘bots’ that can perform repetitive and mundane tasks with accuracy, speed, and compliance – a digital workforce of sorts, following predetermined rules mechanically performing business tasks. Once these clerical type tasks are automated workers can allocate their business intellect and acumen and direct them towards accomplishing activities requiring human touch and knowledge.

What can RPA do?

This digital workforce of ‘bots’ can be tasked via software such as ‘UI Path’, ‘Automation Anywhere’ or ‘Blue Prism’ to open and send emails, login into web applications, input data into forms, extract data from multiple internal data stores, scrape data and follow if this- then that (think of IFFT) type functionality and deliver or email a report. Viola!

Design thinking led Patient-Centric Use cases for RPA in Healthcare

1.  Billing and Claims – These time-consuming administrative tasks can be accomplished utilizing RPA driven ‘bots’. 30% – 40% of claims can be denied due to non-compliance with regulations. The necessary authorizations and paperwork required by healthcare providers to treat and care for patients can be delegated to ‘bots’ eliminating any delays, errors or miscommunication, so the patient/consumer experience is not hindered, interrupted or compromised.

2.  Patient and transactional data – Life science and Healthcare organizations can delegate ‘bot’s to translate, format and input data instead, streamlining and layering compliance with new defenses. These activities performed via RPA would relieve employees to train their attention on tasks that deliver on the patient experience, quality, key consumer insights and building upon the NPS score.

3.  Clinician Notes delivered via speech to text – Built into the new clinician-patient interaction process, an emphasis on maintaining eye contact with the patient is key. It conveys attentiveness – perpetuating an emphasis on patient empathy – a critical value from the patient’s lens. Instead of note-taking, the clinician would switch to audio-recording the patient’s condition, drug usage, vital statistics typically entered manually into the patient’s EHR. In this new interactive process, Natural language processing (NLP) would translate the conversation into text and format it directly into the EHR database via RPA ‘bots’.

4.  Simplification of Patient appointment scheduling – Appointments that are typically scheduled online often encounter scheduling conflicts with different doctors and different hospitals. Cancellations and doctor unavailability lead to tedious and testy phone calls to all parties involved. With RPA – let the ‘bots’ optimally schedule appointments according to the diagnosis, doctor availability, location, and other key criteria. The RPA system would scan the patient data and pass it on to a ‘referral management representative’ to book the appointment. Furthermore, the ‘bot’ can automatically notify the patient if the doctor is running behind or perhaps caught up in an emergency. The RPA software would continually cross-reference the doctor’s schedule and alert the patient if the need arises alleviating the ‘wait’ anxiety. It is a winner. Remember the Patient is at the center of the wheel.

5.  Implementation of discharge instructions – Upon discharge, patients have to follow discharge guidelines, including expectant compliance which may include medications, follow up appointments or an inadvertent adverse reaction from a post-op procedure. Following up on patient compliance can be shifted to an RPA driven process. RPA driven cognitive-behavioral nudges in the form of encouraging incentivized mobile reminders enhances the patient’s experience, compliance leading to a reduction in re-admissions.

Regulatory compliance, efficiency, optimization, and revenue opportunity Use Cases

6.  Recording audit procedures for risk assessment – Healthcare is a regulated industry with multiple tasks and processes which have to be followed up by reports generated for verification, approvals, patient safety and maintaining the quality of services. All these are necessary components of regulatory compliance and can at times result in unintended errors. With RPA – audits can be optimized by RPA including the recording of data, sharing, approvals, and generation of reports meant for multiple entities. RPA can also detect and inform on any non-compliance and violations.

7.  Optimizing and improving the healthcare cycle – The voluminous data collected by healthcare organizations includes key diagnosis insights and treatment cycles. This data is the new oil when layered with data science & analytics. Remarkable trends and brand-new insights plus new revenue opportunities can be derived and have resulted in success. Mature and data-savvy organizations have been able to harvest new revenue streams, some spawning profitable ventures, and startups.

NOTABLE STANDOUT IN THE HOSPITAL SPACE – Setting the pace

The Mayo Clinic is one institution with 300+ AI driven projects with diversified revenue streams from ventures. Resulting success has allowed them to explore, diversify and dip into adjacent ecosystems and are early adopters with a ‘Usain Bolt’ like stride

8.  Population health, remote monitoring & utilization management – There is a great demand for data scientists and universities are gearing up with new graduate programs. This wealth of data which exists, unfortunately, cannot be leveraged by RPA alone. RPA is efficient with structured data only. The Unstructured data which exists in systems like Epic and EHR is massaged with data science statistical modeling and promising results are fed into machine learning systems layered with AI. This lane is wide open for opportunities.

Fig: Open EHR Specification Components Block Diagram

The Future of Healthcare with Innovation and Digital Transformation

The Chief Medical Officer and his/her team are best matched and partner with the Data Science team to constantly explore and test scores of Clinical use cases often resulting in new utilization optimization gold. Payers, Pharma, and Life Sciences are leading the charge with armies of data scientists testing new clinical hypotheses daily, often succeeding in striking virgin oilfields.

Prediction - A Chief Data Strategy Officer + Chief Medical Officer TEAM

A new congruence of likely alliances shall emerge to deliver value

My prediction is that a newly created position of a Chief Data Strategy Officer would likely emerge and tag team with the Chief Medical Officer and his/her team to lead the charge within this relatively uncharted continent. There is a lot of runway in this space and it is just getting started.

The maturity of Cognitive computing, at present, somewhat restrained by Quantum computing lag – when at near maturity should give rise to a V10 muscle car with a 0-100 mph in mere nanoseconds firing simulated human thought processes in a computerized model. Nascar nor the Grand Prix shall ever be the same.

Self-learning algorithms, data mining, pattern recognition with semantic NLP gushing unstructured interoperable EHR data blended with personalization shall flourish. Consider a reality with structured customer data illuminated with luminescent strings of hundreds of personas compared to the 5-10 at best, marketing automation teams fuel consumer engagement with, today.

Would this not be the very Shangri-la of nudge driven marketing orchestrated with masterful accuracy and precision of just the product you had wished for – a perfect selection, paired with an accompaniment you just could not do without, executed, purchased and delivered with a mere head nod.

Oh, what a utopian consumer experience. Pure couture design thinking at its cognitive best.

Could cancer diagnosis and subsequent cures be narrowed down to the make/model/year/type and further broken down to just one of the 3 billion base pairs of the entire genome?

"Genomics mechanics in its finest attire"

Genomics role in healthcare. The why, the what, the how, implications & the role of genomics in healthcare & pharmaceuticals ecosystem.

Have you wondered why you can never delete the Health App from the iPhone? 

Do #getoutofthecube with me. Meet me at ernestpaul@gmail.com . Feel the urge to just say Hello. Just do it. 336.287.1085

I live in Avon, Connecticut. I blog on Digital Strategy topics on Digitalbrine and am a Staff writer for ‘Data-Driven Investor’ on Medium.

Choosing a Customer Data Platform [CDP]

CDPs have become incredibly popular for companies looking to get more out of their data. It’s easy to see why. CDPs help companies unify their data, get a better understanding of their customers and create more personalized marketing campaigns.

Finding the right CDP for your company isn’t an easy process. There are a lot to choose from, but it’s not something that should be taken lightly. That’s why we put together this guide to help you easily identify the best CDP for your company.

CDPs do this by consolidating data from different customer touch points. By breaking down da and bringing together first-party data covering all customer interactions, you can get a detailed, 360-degree view of how customers use your product and what those customers do on your website or mobile app.

A potential customer might start with an organic search on a laptop that leads her to a blog post. The next day, she visits your website again from her phone while commuting to work. Two days later, she signs up for email updates from you. A week later, she clicks through an email for a free trial. Her free trial expires after a week, and then nothing. She doesn’t visit your website again for a month. Eventually, she does come back to your website, and signs up for a monthly subscription to become a new customer.

Without a CDP, that scenario would be hard to track. You’d have all these data points, but it would be stored in multiple places. As a result, you might only know that she took a free trial and then made a purchase. Your CDP brings all of those interactions across different data sources together and consolidates them into a single omnichannel customer profile to help you get a full understanding of customer behavior and engagement.

With that knowledge, marketers can create better marketing campaigns that facilitate customer engagement at the most opportune times. A single customer view enables detailed segmentation (e.g. using demographic or behavioral data) that marketing teams can further use to create personalized experiences and improve conversion rates by targeting the ideal customer profile.

And, CDPs can help with more than just marketing. With a superior understanding of your existing customers and how they use your product, you can improve customer loyalty and retention.

If you’re ready to take that jump and use a CDP to make your company more data-driven, you need to start by comparing different CDPs to find the best one for your company.

Finding the right CDP for your company isn’t an easy process. There are a lot to choose from, but it’s not something that should be taken lightly. Your CDP is going to be handling customer data. Anytime you’re dealing with your customers’ data, you need to be extra sure that their data is safely and ethically handled.

That’s why we put together this guide to help you easily identify the best CDP for your company.

6 steps to choose the best customer data platform (CDP)

Below we walk through 6 important steps that you should go through when choosing a CDP. Following these steps will ensure that you choose a CDP fitted to your goals and resources.

Step 1: Bring stakeholders into the process

Before you even decide which CDPs you’re going to evaluate, you need to bring internal stakeholders into the process. The CDP you choose is going to be working with data from different departments within your company, so it’s important that everyone is bought in.

The question you need to ask yourself at this point is: Who else collects data that your CDP will handle?

There’s a good chance your sales team’s customer relationship management (CRM) platform stores data that your CDP will need access to. A stakeholder from sales should be part of the buying process.

What about your customer success team? There’s a good chance that your customer success team uses tools that handle customer data. A stakeholder from the customer success team will likely be part of this process too.

You don’t need each stakeholder individually evaluating each CDP, but you will need their input on various parts of the buying process. At the very least, talk to each stakeholder and let them know why you’re looking to purchase a CDP and what you hope to get out of it.

Step 2: Define use cases

There’s another big question you need to answer before deciding which CDP is best for your company: What is the reason you’re looking to use a CDP?

It’s easy to get caught up in the fact that you need a CDP because it will consolidate your data into a single customer database, but what are you actually hoping to get out of that? Consolidating your data isn’t going to make you more data-driven. It’s just a step along the way. To choose the right CDP, you need to define your use cases ahead of time.

Take some time to think about what you want your CDP to help with. Then, talk to the other stakeholders about their ideal use cases. From there, try to identify three or fewer ideal use cases. Limiting your use cases to just the top three will make it easier to evaluate all of the CDP vendors.

Here are a few of the most common use cases:

  • Fully understanding our customer journey
  • Creating a more personalized customer experience on our website
  • Creating more targeted multichannel advertising campaigns
  • Combining online and offline data

Once you’ve defined your use cases, spend some time studying your potential CDPs. Look at their website; read reviews of their products; talk to colleagues at other companies who use these tools. Does your ideal use case fit with what any of these companies are doing? If yes, make a list of those companies. At this point, it’s probably going to be a pretty big list.

Step 3: Determine the tools needed

You need to get a handle on the tools your company uses that will be connected to your CDP.

To get an idea of what tools and functionality you’ll need, start by focusing on your use cases. Which tools do you need to accomplish the specific use cases that you laid out in Step 2? Make a list of those tools.

Next, make a list of all the tools that interact with your customer in one way or another. You’ll want to include website tools, CRM systems, real-time live chat, payment processors, email platforms, and help desk systems, just to name a few.

At this point, go to your other stakeholders and double-check that you haven’t missed any important tools that will need to be connected.

Most often, we see customers start with:

Once you’ve determined the tools you need, make sure the CDPs you’re evaluating already have those integrations. If one doesn’t have the majority of the tools you use, knock it out of contention. This step might narrow your list by a large number.

Step 4: Gather requirements

There’s more to a CDP than a way to consolidate data and solve your use cases. You also need to think of the other requirements for your CDP. Requirements are different than your use cases because a requirement is more like a feature, rather than an outcome.

For example, let’s say one of your requirements is that the CDP you choose should help you get a solid understanding of each piece of data that you’re collecting. To pull that off, you’ll need a CDP that can help you build a data-tracking plan.

If you’re not sure what other requirements you need to consider, here’s a list of common requirements that our customers have:

  • We’d like our CDP to help with GDPR and CCPA compliance. If that’s something you’re interested in, then you’ll need a CDP that will that will enable you to suppress data collection or delete customer data when requested, which is a requirement for both the GDPR and CCPA.
  • Our CDP should help us get a full view of our customer journey. If this is a requirement for your company, make sure that the CDP you’re evaluating has some form of identity resolution, which helps identify users across different channels.
  • Our CDP needs to have top-notch security. This is becoming a more frequent requirement. Make sure the CDP you’re evaluating has a credible, independent security certification like ISO 27001 or SOC 2. Those certifications ensure that the CDP is continuously monitoring and upgrading their security practices.

Another good place to gather requirements from are the pricing pages of each CDP. Read through the features that are listed on those pages, and make a note of anything that’s going to be important to your company.

For example, you might see that one CDP has an uptime guarantee, while another doesn’t. If an uptime guarantee is important, you might want to make it a requirement.

Step 5: Compare vendors

At this point, you should have a list of just a few CDPs that fit your use cases, have the necessary integrations, and meet all of your requirements. Now, it’s time to compare each CDP. Don’t take pricing into consideration yet. We’ll get to that in the next step.

Start by considering your industry. Find CDPs that have customers most similar to your company. If you work at an enterprise-level company, find a CDP that has a track record of working with companies at that level. If you work at a startup, make sure the CDPs you’re evaluating have experience in that space. Chances are there will be an overlap with CDPs that have a track record in all industries, but that’s okay.

If you’ve determined that all of the CDPs you’re considering have the right experience, it’s time to go a step deeper. Make sure each CDP has:

  • A track record of accomplishing the use cases that you defined in Step 2.
  • A solution for data compliance. CDPs handle data, so they should enable your compliance with the GDPR or the CCPA.
  • The right integrations for your current and future use cases. Is each CDP continually adding new integrations to their integration catalog?
  • Excellent customer service to help you set up, use, and maintain your CDP.

Don’t forget to look at review websites for user reviews of each CDP too. G2and Capterra both have dedicated pages for CDP reviews.

Step 6: Consider ROI

The ROI of the CDPs you’re evaluating is the final piece you need to consider. ROI doesn’t mean that you should choose the cheapest option. It’s more about which option will give you the best value. How do you determine that value upfront, before you choose your CDP?

Start by using our ROI worksheet. This will help you determine the cost of your engineers’ time. Without a CDP, your engineers have to spend hours building and maintaining integrations for each tool. Those hours add up quickly, which can result in significant costs just to build and maintain one integration. If you have ten integrations that need to be handled by your engineering team, you can see that the hours will quickly become unmanageable.

That cost is one of the biggest reasons to use a CDP. Good CDPs should reduce the amount of time your engineers spend building integrations between tools, which can result in a huge cost savings.

That’s why you need to calculate the costs and consider ROI ahead of time. If you choose a CDP that doesn’t give your engineering team the maximum amount of time-savings, it may not be worth the cost at all.

What CDP do you need?

Choosing a CDP isn’t a quick process. You need to make sure you’re doing your due diligence to find the right CDP based on your specific use-cases and requirements.

Once you’ve done that, you’ll be able to get more value out of your data and get a better understanding of your customers. Plus, your engineering team will thank you for reducing their workload since they won’t have to spend time building and maintaining integrations with your tools.

Courtesy: Segment

The quest to deliver Customer Personalization – Marketing Technology

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Brands have to identify and understand each and every customer 

Ever been to a restaurant where they know you by name? and the waiter happens to know your preferred drink of choice and like magic brings it to your table while you are getting seated. The ‘Cheers’ kind of experience for those of us who recall the sitcom. These one-on-one personable interactions make experiences special.

We live in a digital world now and demand similar experiences from brands. We want to associate with a brand who knows us. We gravitate towards these brands, rare as they might be. However such a rendezvous brings familiarity & comfort. Trust soon follows. I recently called up my banking institution over the phone, a few taps on the phone initiated by me and there was a friendly voice greeting me personally instantly. Voila! All done with voice print. We expect similar experiences, minimalist, natural, efficient and transferable across devices.

"Patience in today's hyper connected world is on a diet due to clumsy marketing tactics"

What do customers want from brands?

On occasions while dining I have often witnessed all individuals seated at a table nearby simultaneously glued to their mobile phones. Digital and mobile may have taken us all by storm long ago but we do seem to prefer company even if it of the silent variety. This mobile solitude may be amusing to some, not marketers. To marketers, this means real time location data, related contextual information including weather, possibly to trigger a nearby physical store location reminder.

Customers want relevant, meaningful and tailored information and offers from brands who meet their specific needs. Irrelevant offers and emails induce customer paralysis and prove to be counterproductive. In this era of instant gratification and attention span deficit, there is little room for off target customer communications. With a thorough understanding of customer needs, both parties are likely to benefit.

Customers have long declared their expectations. See Figure below:

Delivering on customer expectations enhances customer engagement leading to loyalty for brands, resulting in reduced acquisition costs, a revenue upside and increased retention rates.

The journey begins with data

Brands seeking customer-centric nirvana have to become data-centric, first. None of this degree of hyper-personalization at scale can be accomplished without customer data.

"Data is the new oil. Refining or anticipating customer needs is the beginning of personalization"

This crude oil or rich behavioral customer data, customer interactions, social media activity, demographics, customer life cycle stage recognition and transactional data can be segmented and further sub-segmented. Personalization dividends can be harnessed today without a full blown implementation of technology such as a Customer Data Platform, a CDP. Existing data can be used in cross-sell initiatives, enablement & activation of a few consumer use-cases based on past purchases and behavior. Sometimes using less data is more effective prior to incorporating external purchased data sets.

Incorporation of 2nd & 3rd party external data adds dimensional layers to the refinement process and further enriches customer data. Multiple digital identities, which many consumers use online can be merged into a single record to eliminate redundancy. Iterative cycles of customer behavior and interactions captured via analytics continually refine customer data. Behavior solidarity within customer ‘data sets’ feeds pattern discovery and recognition. With confirmation and accuracy of patterns, machine learning kicks the ‘data sets’ up a notch. With continuous cycles of deep learning & artificial intelligence, predictive analytics begin to unlock future customer behavior. 

How far along is your organization in channeling this new oil, 
piping it, refining it, triaging it, harnessing it to extract
measurable value?

Agile, cross functional teams – marketing, tech experts & operations must work together

Working in silos is kryptonite for personalization. Having a cross functional team in a test and learn mode, sharing insights within a ‘not afraid to fail culture‘ is the best environment to productively deliver, preferably in a war room like setting.

The personalization DNA resides in behavioral data. Its application rests on a thorough mapping and intersection of customer journeys, triggers, devices, events, marketing campaigns and collateral aligned to customer segments with matching behavior. A cross functional team serves this proposition well.

Marketing & Operations realignment

Organizations must look at the personalization ecosystem in its entirety, from data manage­ment to advanced analytics to customer en­gagement, through to measure­ment and optimization. Marketing resources are generally organized by specific skill. For example: Analytics or Campaign management or even by Chan­nel – Social or Search. There is an unintentional siloed ecosystem risk to be wary of. Although optimizing for different elements is import­ant, the whole is greater than the sum of its parts. An understanding of how the different parts interact and how to inte­grate them to support personalization is what differentiates high-performing marketing organizations from poorly performing ones.

Building the customer journey

Grouping customers together with matching needs and behavior is a good place to begin. Armed with a handful of these groupings or segments, align each segment with its own customer journey & map the series of interactions with the company brand. Examples: Visits to a broker, agent, company website, calls to a call center, social media posts, even tracking prospect visits to the company brand’s competitor, etc.

Building customer segments

Hundreds of mini-segments may emerge as a result of combining journeys and customer segments. Each mini-segment may be nuanced and one more valuable than the other. Each should be considered & prioritized by its relative value. For example: Consider a leading insurer who may find it more valuable to engage with its customers who are within their ‘renewal window’ by sending them a reminder that their policy is nearing expiration. Rather than pushing them towards a cross-sell product and risk losing the customer to a competitor, the insurer chooses to send a limited-time policy renewal loyalty-offer.

Harnessing customer signals

The customer provides signals as to their intent with their online & offline interactions. Mature predictive analytics catch these signals and push them into a workflow to be followed up by an appropriate response. Each signal, nevertheless deserves a response, a timely and relevant trigger message, to close the loop.

Signals & Triggers at work

With advanced and meticulous planning, a library of signals and matching triggers have to be maintained and kept up to date. Each trigger with matching collateral can then be dynamically executed. Each of these combinations are continually refined & optimized by analytics. Upon a valid declaration, each becomes a business rule. For example: Consider a leading health insurer who learns of a dependent soon to be in the ‘dependent turning 26 window‘. The customer and/or dependent promptly receives a triggered message with a limited time offer towards a new personal health insurance policy for the dependent.

Pharma & Life sciences regulatory caution is giving way to customer personalization

Regulated industries in Pharma, Life Sciences, Healthcare and others have been increasing personalization related spend. Even with an innate regulatory driven caution, the portfolio trajectory is shifting from clinical trials customer data spend to customer/patient experience related personalization spend.

Customer data & personalization together aid in the identification of high-value patients, who can then be channeled towards physicians and therapists, realizing a tailored & personalized formulary experience. 

Insurers have personalization high on their portfolio spend agenda

Insurers are attaining underwriting efficiencies, realizing a not so far off dream of instant insurance for Health, Life and others. They are now providing customers Population health efficiencies and Genomics focused underwriting, all a result of data personalization. The race is on and competition is rife. Personalized internal & external customer data, including IoT customer data can now reside within a CDP or a similar configuration.

Stitching the Personalization DNA within your marketing technology stack or with an addition of a CDP

Achieving growth on a large scale, across all channels and geography requires immense preparation, alignment, governance and most of all leadership and talented human capital. 

Rerouting existing operational organizational alignment, restitching operational processes and RPA driven workflows, scaling across the company incrementally and globally requires a well-articulated blueprint with an equally agile playbook.

New marketing technology configurations equipped with a ‘smart brain‘, ready to direct traffic with rules & algorithms, can finally foster a new breed of a one-to-one marketing reality.

Avoiding pitfalls and traps

A series of steps may be necessary to arm an incumbent MarTech stack to minimize ROI leakage from new technology investments.

It is easy to rack up a bill in the millions on data integra­tions that magnetically pull all this information together into a data lake, prioritizing the most valuable types of data—the kind which drive the high value use cases. Identification of the required latency for each data element is critical at this juncture. Most use cases require real-time information for a limited set of data elements, so most real-time capability can be thankfully decelerated.

Discover business use cases which the technology would drive, not vice versa

Buttressed by an agile development process formulate business use cases supported by KPIs, business drivers and forecasts. Existing digital analytics would aid in benchmarking, revealing delta variances and future forecasting. The market­ing process is iterative, optimized and improved with each cycle. 

Collaborate closely with the Marketing teams to uncover functional requirements

In close collaboration with the marketing operations team, functional requirements must be well articulated and documented. Juxtaposed against the numerous technology solutions & providers which make up the MarTech landscape, this step is a cornerstone effort. Multiple use cases, storyboards or multiple contexts help in identifying a certain missing technology component. This good catch may lead you to a different vendor choice and selection when augmenting your technology to fit your personalization goals.

Align organizational needs and goals with a technology approach

Major integrated Marketing Cloud suite vendors including Oracle, Adobe and Salesforce do not support the personalization ecosystem end-to-end. Brands may already have one of these suites in place. In that case, a reasonable compromise would be to extend the capability of the existing suite with the best of breed of each functionality element missing from the existing MarTech stack. The downside would be a less than tight integration to the element supporting that specific functionality or service.

A key consideration for the analytics engine – The analytics engine ‘the brain ‘ should be customizable including the algo­rithms, data features and business rules specific to your requirements. The solution set should allow you control over the inputs to the analytics engine.

Build incrementally and via pilots 

A prudent approach would be to set your sights on an iterative and incremental value delivery approach supported by well-defined pilots. With each new element added for a new use case, costs rise.

However, the incremental value attained would likely align with the incremental investment, thus justifying the investment. A phase driven and stage-gated approach would smoothen the decision making for the CFO to fund the incremental spend.