There is a vibrant stride underway for the application of AI in marketing. Brands who are adopting AI are engaging with customers in a way which they want. The brands have greater insight with data and user experience [UX] making conversations possible.
Google is on a ‘code red’ got caught basking in its search monopoly. They have called in the founders to weigh in on the Open.ai & ChatGPT threat to their dominant position in search.
I/ we have been quietly mulling why searching on google is not a rapid process. It is best to anticipate customer needs, wants and the unidentified needs. ‘I/we’ are the customers in the image below. When I ask myself ‘how well am I served by google’ ? I would say – Not well at all. Google has woken up to a competitive threat rather than being in tune with the customer. Some of you may remember the browser ‘Netscape’ and how it was a threat to Microsoft. It led to a better browser. Well, we need a better search experience here. It is the smaller companies who innovate and seem to wake up the giants. That is a sad commentary for American technology innovation.
I searched for – ‘AI use cases for marketing in an essay’.
You be the judge of utilizing AI for content creation. A copy and paste may not be advisable. In conjunction adding your own content is an upgraded strategy. Unfortunately there is no emotion in the writing style (differentiates from human vs AI driven). Compensate for the ‘blah’ and robotic voice and tone.
Predictive analytics: AI can be used to analyze customer data and predict future behavior, allowing marketers to target their campaigns more effectively and improve ROI.
Personalization: AI can be used to personalize content and recommendations for individual customers, improving the customer experience and increasing engagement.
Chatbots: AI-powered chatbots can be used to provide 24/7 customer service and support, answering questions and helping customers navigate a website or app.
Image and video recognition: AI can be used to analyze images and videos, for example, to automatically tag and classify visual content for better organization and searchability.
Voice assistants: AI-powered voice assistants can be integrated into smart speakers and other devices to provide hands-free interaction with customers, allowing them to make purchases or get information quickly and easily.
Social media monitoring: AI can be used to monitor social media for mentions of a brand or product, allowing marketers to respond quickly to customer feedback and complaints.
Email marketing: AI can be used to analyze customer data and predict which customers are most likely to open and click on an email, allowing marketers to send targeted campaigns and improve their email marketing ROI.
Campaign optimization: AI can be used to optimize ad campaigns in real-time, adjusting targeting, bid prices, and other parameters based on performance data to improve results.
Fraud detection: AI can be used to detect fraudulent behavior, such as click fraud, in online advertising, helping to protect a company’s advertising budget.
Content generation: AI can be used to generate new content, such as product descriptions or social media posts, saving time and effort for marketers.
Ok good. What did you think ? Worth or not worth. The hoopla about school essay & AI manufactured content aside
Seriously, the navigation has the potential to be a minimalist & frustration free search journey. With, stellar results it will be the bomb.
We don’t really need semi-conducting to power up the speed of search. The flavors in this ice cream are good. In fact they are more than good. I can anticipate saving hours which I spend distilling ‘Google’ search results.
pertinent search | S Ernest Paul
The next iteration of the search experience has begun. The battle of the giants it is. Google versus Microsoft. The alarm bells for a $160B search pie. I have a feeling we are going to be the beneficiaries in the end.
A simpler interface powered by AI aid coming delivering a laser focused singular search result
OpenAI’s opportunity is to narrow search to the most relevant response and save consumers the painful experience of scrolling and picking links to click on.
As marketers we understand that a marketing plan is the most important and strategic document we will produce all year. The problem is we don’t always treat it as such.
For many of us, the urgent seems to get in the way of the important and we end up rushing to complete our plans at the end of the year to make deadlines associated with executive team presentations. We often create the plans in silos with little input from sales or the product team. For those of you who have been with a company for more than a year, were you lazy and started with last year’s plan and built the current plan off of it? Be honest.
CMOs that have teams with more than 5 marketers usually have several functions to manage. The problem is that each of these functions are often focused on what they do well and not the greater goals. For this reason, marketing teams can have misaligned goals and campaigns tend to be function-specific rather than focused on the target audience and message. This dramatically impacts the effectiveness of the marketing plan.
Even when you have a plan, the team doesn’t always follow it or know how to apply it to their function. How many times in the middle of a planning cycle have you heard an event’s person say, “I think we should run a dinner series” without any context to the plan or why or with who? Or a digital marketer says “Let’s do an email program to the database” without thinking about segmentation or tying it to campaigns outlined in the plan? These are one-off tactical activities that are marketing-channel specific. More probable than not, these efforts failed, or at least did not meet expectations. The question is, why?
There are five primary reasons why marketers run rudderless marketing activities and do not follow the plan:
1.The plan that was built at the beginning of the year was not detailed enough for the team to use as a guide for their efforts
2.The team never fully understood how the strategy fit with their function, so they defaulted to what they know how to do instead of doing what aligns with the goals and strategy
3.The plan was solid, but it resides in a presentation deck somewhere, never to be seen again
4.Each member of the team built his or her own plan, and those were never integrated across the functions
5.There wasn’t a comprehensive, goals-driven plan
If there wasn’t a plan in place, which unfortunately happens far too often, then there will probably be a change in marketing leader- ship soon. All of the other scenarios listed above are direct failures of the CMO not setting a clear strategy, getting team buy-in, and continually reinforcing the direction by revisiting the plan.
To ensure you do not get caught in the busywork marketing cycle and are aligned with the marketing strategy, there are a series of questions you need to ask when new ideas, campaigns and programs come to light:
What is the goal we are trying to accomplish?
What is the right strategy to accomplish this goal? Who is the target audience?
What are the messages we want to deliver to that target audience based on their needs?
Does this tie into a larger theme? What are the metrics of success?
If the answers align with the current plan and you are practicing an agile marketing approach, then you should consider the new initiative. But caution, if the conversation gets tactical and stuck on the marketing channel of delivery without providing answers consistent with your plan, walk away.
What is a marketing Plan?
Let’s take a step back and talk about the definition of a market- ing plan. In early 2020, Wikipedia offers the following:
“A marketing plan may be part of an overall business plan. Solid marketing strategy is the foundation of a well-written marketing plan so that goals may be achieved. While a marketing plan contains a list of actions, without a sound strategic foundation, it is of little use to a business”
After reading that definition, maybe this is why marketing is of “little use to the business.” When done correctly, there is no “maybe part of an overall business plan,” it is a large portion of the business plan. And you need to think and prepare that way.
Companies that sell to consumers (B2C) usually view marketing as the most strategic function at the organization. For companies that sell to other businesses (B2B), marketing can be viewed as a support function for sales. In either case, the foundation for the marketing plan is the same: you need to identify the right buyer who s a need for your product, and you need to deliver a compelling message to inspire them to purchase.
S. Ernest Paul
Every comprehensive marketing plan should include the following strategic marketing element in this order to build off each other:
1.Situational analysis (historical data)
2. Market research and analysis
3.Company goals
4.Marketing goals (roll-up to company goals)
5.Marketing strategies
6.Target audience (segmentation and need)
7.Positioning and messaging
8.Product and services direction and definition
9.Pricing and packaging
10.Competitive analysis
11.Sales channel strategy (distribution model, customer acquisition and lifetime value
19.Marketing team structure/growth/responsibilities (org chart)
20.Technology (software)
21.Budget allocation
22.Testing (messages, ideas, markets)
23.Metrics of achievement
24.Assumptions, dependencies, risks
Unless you are the head of marketing or marketing operations, you may not be responsible for all these plan elements. However, every person plays a part in the success of the plan, so work with your team to carve out your role.
Building an Agile Marketing Plan
Let’s say you and your team have just built the best plan ever using the Marketing Plan Framework (MPF). The plan fully aligns with the goals as they stand today and details a comprehensive strat- egy for achieving them. You start executing the plan to perfection and then, out of nowhere, the roadblocks to success start to appear. Before you know it, you are off course.
The list of reasons why your plan can crumble is long. Below are some common causes:
1.Economic volatility causes budget cuts
2.Competition comes out with a new and improved product or revised pricing
3.You have employee turnover of key marketers on your team
4.Your plan isn’t achieving the stated goals
5.The sales team is not prepared to do its part
6.The CEO changes the company direction
7.The R&D team does not hit its release dates
8.The industry you sell to starts drying up
9.You fail to get traction in a new geography
10.You have to overspend on a campaign, so you need to modify the plan
11.The team does not understand or buy into the plan
12.You hire the wrong skill set or onboard someone late, creating a capacity gap
13.New hires don’t pan out, leaving you shorthanded
14.Vendors you hire don’t understand the plan and have mis- aligned output
15.The sales team decides to take a different approach or direction
16.Your partners do not hold up their end of the bargain Lead generation emergencies arise and distract the team
18.Campaigns and programs get delayed creating a ripple effect downstream
19.Major marketing events get cancelled 20.Marketing leadership changes
How many of these have you experienced? Hopefully this list did not send a chill up your spine, but these are the key contributors as to why CMOs have the shortest lifespan in the C-suite. So what does this mean? It means you need to build a plan that is flexible and prepares for different scenarios. In other words, you need to build an agile marketing plan.
Why do you need to need to be agile? Because stuff happens— both good and bad. In either case, your plan can get back burnered while full effort is put toward taking advantage of the new oppor- tunity or resolving the current issue at hand. All too often, after the disruption subsides, the plan is derailed, and the team engages in rudderless marketing activities hoping that something works. The problem is that hope is not a strategy, and more often than not, this only results in unachieved goals
An agile marketing plan is made up of 3 key components:
1.Flexing for opportunistic marketing
2.Underachievement scenario planning
3.Overachievement scenario planning
Flexing for Opportunistic Marketing
Depending on your company size, numerous unplanned opportunities will emerge over the course of the year, such as: a customer wants to do a press release with you, an industry analyst ranks your
product or service the best on the planet, or a partner wants to OEM your product and do joint marketing.
In marketing, you are constantly working with the outside world. The problem is that you can’t control what these external audiences do or the timing of the opportunities they place at your feet. You can only control your end of the equation.
Typically, there are three reasons you pass on unplanned opportunities:
timing, resources, and budget. But if you have that information at your fingertips, then you can quickly compare a new opportunity against the current plan to determine which will have more impact on the goals. For this reason, the key steps to the evaluation process are:
Assess if the opportunity helps to achieve the annual marketing goals and is executable
Prioritizeby comparing new opportunities against existing marketing campaigns
Collaboratewith the team to plan for new oppor- tunities and modify existing campaigns
Reallocatefunds accurately without going over budget
Re-engagewith the original plan to get back on track
A word of caution: Don’t let frequent urgent opportunities distract you from the original goals-based plan, but be flexible if the opportunity is too good to pass up.
There is a seven-step process for building and executing scenarios:
1.Identify the driving forces behind the potential risks, issues, or decisions
2.Determine the impact to the current goals
3.Create a new set of goals that map to underachievement or overachievement
4.Rank the strategies, campaigns and programs by criticality (highest impact to the business), keeping in mind budget thresholds (especially in case of cuts) a.Align the ranked marketing initiatives to the scenarios based on severity up or down b.Assign a numerical ranking or categorization such as keep, consider, cut
5.Build a tiering structure based on the level of under or overachievement
6.Select metrics for monitoring and create thresholds for when scenarios are triggered
7.Assess the impact of switching to the scenario and adjust accordingly to alternative strategies
Alternative strategies for overachievement are limitless, but when you underachieve, human and financial resources usually get tight. There are some inexpensive marketing options to explore if you have to put together an underachievement plan. Switching from paid to free marketing is the best place to start. Strategies such as content, social, viral, word of mouth, and joint marketing with partners (splitting the expenses) can be very cost effective and will stretch your discretionary spend. If the cuts are primarily to headcount, reallocating financial resources to AI-related marketing software can create lots of efficiencies across the board. You can also look at cheaper offshore vendors for activities such as design, SEM, and telemarketing.
Marketing Plan Template
PlanElement
Plancontents
Situation analysis
Market research & analysis
Company goals
Marketing goals
Marketing strategies
Target audience (including segmentation)
Positioning and messaging
Product and services direction and definition
Pricing and packaging
Competitive analysis
Sales channel strategy
Sales support
Partner/channel strategy
Product and services launches
Campaigns
Marketing channels (vehicles)
Programs Marketing activity timeline Team structure, growth and responsibilities Technology (software) Budget allocation Testing Metrics of achievement Assumptions, dependencies, risks to success
What does it take to be a great CMO? Vision and creativity are important, but “operational marketing” is essential if you’re going to make your vision a reality. Perhaps the most important job of a CMO is to orchestrate all the parts of their team to do the only thing CEOs really care about: deliver results!
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
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 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.
Fig: Revenue and efficiency opportunities to discover trends and key insights
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!
Fig: The PDCA RPA Recommended Process Model for Continuous Improvement
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 Notesdelivered 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.
Fig: Recommended RPA initiatives consensus-driven decision model for governance and compliance
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?
