The tool quickly attracted techies and non-techies because one prompt can generate a response usable by editors, PR teams, developers or executives to create white papers, software programs, client presentations, press releases and more.

As marketing professionals, it’s critical to learn what it can and cannot do today, how best to use it for our campaigns — and what’s in store for us.

What ChatGPT can do today

Launched by OpenAI in November, ChatGPT is built on top of OpenAI’s GPT-3 family of large language models (LLM) and enables interaction with a model via a conversational user interface. 

ChatGPT computes the next most probable set of letters or words when given an initial starting phrase or “prompt.” It was trained on 300 billion words taken from books, online texts, Wikipedia articles and code libraries — reportedly using a snapshot of the internet as of 2021. 

“As a language model, ChatGPT is best used for tasks that involve natural language processing, such as text generation, text completion  and answering questions. Specifically, ChatGPT can be used for a variety of tasks including:

1. Chatbots: ChatGPT can be used to power chatbots and virtual assistants that can engage in natural language conversations with users.
2. Content creation: ChatGPT can generate human-like text on a variety of topics, which can be useful for content creation, such as writing articles or generating product descriptions.
3. Question-answering: ChatGPT can provide answers to questions posed by users, such as providing information about a particular topic or field.
4. Translation: ChatGPT can be used to translate text from one language to another.
5. Text summarization: ChatGPT can be used to summarize longer pieces of text into shorter, more easily digestible summaries.
6. Personalization: ChatGPT can be trained on specific datasets to provide personalized responses or recommendations based on user behavior or preferences.

Overall, ChatGPT is a versatile tool that can be used in a wide range of applications that involve natural language processing.” 

This section was generated by ChatGPT with the prompt: What is ChatGPT best used for?

Today, the outputs of ChatGPT are impressive in documentation areas. It can assist — but not replace — humans in writing reports, outlines, creating press releases, books or developing surveys. It helps writers and editors get started in developing pieces of content. When you have writer’s block and are stuck, ChatGPT can come to the rescue.

Dig deeper: When I asked ChatGPT to write an article about ChatGPT

What are ChatGPT’s limitations? 

As the log-in page of ChatGPT points out, it does have limitations, like any new technology. ChatGPT may occasionally generate incorrect information, including harmful instructions or biased content.

Remember, this tool is based on a snapshot of the internet in 2021, a year of pandemic mania, racism and unmitigated lying. The tool is only as good as the data that supports it. That’s why it cannot answer questions about events that happened from 2022 to date.

Below are some other areas where ChatGPT has limits:

Search

At best, it is a complement to search and only a partial alternative. ChatGPT focuses on generative approaches to answers rather than artifact discovery (e.g., a particular document or sentence). It also does not tell you from what source it pulls the information, meaning anything it reproduces is not attributed to the original author. 

Language translation

ChatGPT can translate simple sentences into traditional languages. However, it can have problems with translations for domain-specific areas and languages well beyond the Mediterranean languages. A human translator should be engaged to validate the translation.

Privacy or confidentiality 

ChatGPT users should treat the information generated as a public site post and avoid publishing personally identifiable, company or client information. Users’ conversations with ChatGPT could be used for training new models and will be reviewed by trainers. You can’t delete specific prompts, so be careful what you share. While you can delete an account, this will not delete the training data.

Accuracy of output

Users should carefully evaluate the inputs and outputs of the tool for misrepresentations and biases. ChatGPT is enhanced to align with the trainers’ preferences rather than verified facts. This means that output is plausible but not reliable for many use cases. Moreover, bias might be present in the large datasets that train the model. 

Here’s an example close to home. I used ChatGPT to generate a statement about my accomplishments and background. For the most part, it was very accurate, but when it had me starting my career as a software programmer, I immediately saw the tool’s limitations. Since I was in the technology business and had worked for several large tech companies, it inferred that I must have started as a software engineer. I didn’t. I started in marketing.

Dig deeper: Three things ChatGPT needs which only you can provide

How marketers can best use it

Knowing ChatGPT’s strengths and weaknesses can help you decide where to use the tool. Since natural language processing (NLP) is what it does best, start with the tasks that you might need as a marketer.

• Writing point-of-view papers.
• Developing website content.
• Updating social posts.
• Creating blogs.
• Writing bylined articles.
• Generating press releases.
• Writing program or project documentation.
• Developing a marketing strategy.
• Drafting legal agreements (first draft of your requirements to give to your legal department).

Usually, half of your time developing some of these pieces is spent researching. ChatGPT will be a good go-to for research assistance if you remember the limitations above. 

Here are two last pieces of advice when using ChatGPT:

• Develop a prompt that will narrow the output for the tool. I’ve found it works best when the topic is specific and ask it to simplify the response.
• Think it will save you time? You may be disappointed. It is best used as a “hole” filler if you are limited on staff or researchers. But if you have a professional writing staff, then ChatGPT should be viewed as any other tool they would use to help refine their craft. View it in the same category as spellcheckers or Grammarly. 

What marketers can expect in the future

ChatGPT belongs to the category of growing generative AI tools, where you can also find Dall-E, which generates digital images from prompts. In February, Google announced it would enter the market with Bard, its version of ChatGPT. From what we know of the two tools, here are the key differences: 

As this tool category expands, expect better outputs and current references. (Google will use its own data set to train Bard). The critical question is: “Will it replace marketing writers?”

No. Like all tools, it will supplement and complement the writers’ capabilities but not replace them. After all, there is a limit to what a computer can create.

Sentient computers are still several decades away. In the interim, we need to get used to using tools like ChatGPT because the limitations of technology will always be in the hands of humans.

Dig deeper: Does ChatGPT pose an existential threat to marketers?

The post ChatGPT: A marketer’s guide appeared first on MarTech.

For example, high-tech companies like Google are built on the value of their data. Google began by analyzing data from the web. With its search engine at the heart of data collection, it could offer data gathering of adjacent fields and analysis from that data. Every acquisition Google makes adds to personal information gathered to serve their markets. It is data that powers their advertising supremacy. 

Google, Amazon, Apple and Microsoft combined are worth $5 trillion. That’s roughly the value of the Netherlands. So, what do these high-tech companies have that keeps them growing?

Data. They have data:

• About consumer purchases.
• From devices that they develop.
• From services that they provide, such as cloud, gaming, etc.

All this data has a value that contributes to their overall value as a company.

But your company has data as well, data that can probably be just as valuable to your business as Google’s is to theirs. Here are some steps to ensure you derive the most from your company’s data.

1. Assess the market for your data

Before you can value your data in the marketplace, you need to determine if you have an asset that others might want and that the asset can be made available to others. Let’s tackle these situations separately. 

Do you have an asset that others will pay for? 

Just because you believe that your information is golden doesn’t mean that it is. For example, if you own a tractor company and you collect IOT data on these tractors, you might believe that your dealers would pay for information that predicts when parts might fail. But the dealers may think that that information is something they should have anyway because it allows them to service your product better.

Let’s look at other data assets. Suppose you have collected information for years about which URLs on the web belong to suspicious actors. Would that information be valuable to security application providers, security officers, the government or the private industry? Do you know the answer?

Here’s a quick calculator that will give you an idea of how to look at personal data — your data. This calculator was developed by the Financial Times and helps you to understand how valuable the information is that you possess. This is a beginning step to understanding the value associated with data. 

Like all good product managers, you may have to conduct market research to determine what market segments your data appeals to and how much those segments will pay for the information. With this information, you can begin to “package” your data product to sell. 

Do you have the right to ‘sell’ the data you have? 

When marketers think about buying data, they usually jump to thoughts of customer lists with vital email addresses. That kind of data has been collected under regulations like the California Consumer Protection Act (CCPA) or the General Data Protection Regulation (GDPR) of the European Union. 

With both overarching regulations, you first need a legal basis for processing personal data on your customers. Consent — or opt-in — is the most common legal basis. But remember, consent means that personal data can only be used for the purpose for which it is collected. 

To use it for any other purpose, you must inform the customer of your use. So, if you plan on selling your customer data, you need to have informed your customers when you asked for consent to market to them in the first place. Otherwise, you need to change your privacy statement. 

Most of the time, your intention on how you will use the data is included in a privacy statement. But if it’s not there today and you want to sell customer data, you cannot simply change the statement without communicating to the customers about the change. You must give them the opportunity to opt out. This action could impact the amount of data you retain with the ability to use it and, hence, your data revenue. 

Dig deeper: Why data compliance is more than consent management

2. Assess the quality of your product

Let’s now look at what every product owner must do to ensure quality products. First, they need to assess the quality of the data they have determined can be sold. Remember that data has some qualities that should be managed regularly. These should include:

• Accuracy: How well does the data field represent reality? Is the information in the field valid?
• Completeness: How many of the fields in your data set actually have data? 
• Coverage: How well does the data set you will sell cover the market segment or use case the buyer needs?
• Consistency: How consistently is the data formatted? Does your data represent dates in the same format, or does it abbreviate street addresses in the same way?
• Duplication: How many of the records in your dataset are duplicates? Buyers don’t like paying twice for the same record.
• Timeliness: Does the data set you are selling consist of recent data or a mixture of old and new? What is your buyer interested in?
• Detail: How detailed is the data set? This is especially helpful if you are selling unstructured data gleaned from web transactions. When selling images, ensuring that they are tagged appropriately and consistently is a detail most buyers require.

Most buyers will insist on a quality report of the data purchased. So, now you have an identified data set of sufficient quality to entice a buyer. What do you do next?

Dig deeper: Why clean data is key to organizational success

3. Develop a strategy for ROI 

Countless questions may need to be answered in the strategy that you develop for the sale of your data. Here are just a few:

• Will the data be purchased on a by-use basis?
  • In other words, the buyer gets exclusive use of the data for a particular event but must not use the data again for another event. 
• Will you make the data set available as a service?
  • In this case, the buyer would have access to the data as needed, but the entire data set would reside with you, perhaps in a cloud dedicated to this activity.
• Can the data set usage be transferred from a company to that company’s partners or resellers?
• In what format will the data be made available? Will it be transferred via FTP or some other way to ensure security?

The most important question to answer is: Why are you considering the sale of your data? Is it to provide an additional stream of revenue for your company? To help foster better relationships with your partners? To establish a new business within the company? Being clear on your purpose for selling the data is primary.

The next phase in your strategy development is ensuring you know why the customer would buy your data. This may require that you do some primary research with the intended buyers to determine not only if they would buy the data but how much they would be willing to pay for it and how they would want it to be made available. Driving a primary research effort helps to gain clarity for this venture.

Then, you need to evaluate your expected revenue against the expense to package, market and monitor the sale of data. Often, organizations forget that this is a specialized effort and cannot merely be managed by the data team in place for day-to-day efforts. Selling a company’s data requires full support from across the organization. 

Before making any major decisions about why or how to sell your data, read Doug Laney’s book “Infonomics” which will give you a good background on all the considerations when turning data into a real asset.

Dig deeper: How to improve marketing ROI with clean data

4. Develop organizational support for data as a product

With an ROI for this venture, you’ve taken the first step to develop organizational support for data as a product. Selling data will require an operational structure, marketing programs and reporting. To make the structure happen, you will need budget, people and executive support. Executive support is the most important of those requirements. 

Organizational support also extends to the team responsible for managing data and ensuring its quality. The outcome of their job moves into the spotlight as quality metrics become visible internally and externally. Managers and subject matter experts should understand the implications of this new view on their work. 

And, if your organization does not yet have a governance capability, executive management will undoubtedly request accountability on some scale. That leads you to the final step.

5. Establish governance over the operation 

Whether your organization has created a governance council, data policies or just ensured that the data team is well managed, beginning to sell your data places a new emphasis on the accountability for data. This usually requires a formal structure or process. 

Data governance is a collection of processes, policies, roles, metrics and standards that ensures effective and efficient use of information. If you find that you need help in understanding how to create a governance program and maintain it, please read John Ladley’s book “Data Governance.” 

Not all value from data is derived by selling it on the open market. Data can provide value in other ways. At the start of the COVID-19 pandemic, American Airlines used the value of their Advantage data to secure two loans from the government under the CARES act. The loans were valued at $4.5B each. At the time, American Airlines said that third-party evaluators had appraised their data at between $19.5B and $31.5B. 

Whether you sell your data, use it to secure loans or just make your company operations more effective, the first steps are the most important:

• Determine what you want from your data.
• Prepare it for use or sale.
• Garner support from your executive team.

The post 5 tips to extract true value from your data appeared first on MarTech.

The relationship between IT and marketing, especially marketing data, is one example affected by constant change. Here’s how.

A look at IT and marketing data’s relationship

Technology is developing exponentially. Emerging tech will experience a growth of 104% by 2023, according to Statistica. That growth is in a market representing hardware, data centers and semiconductors that reached $1.4T in 2021.   

Now couple this technology growth with the ever-changing face of marketing where technology is having a profound impact. More marketing tasks are being automated requiring fewer people. Artificial intelligence dynamically manages customer responses and requests. Experiential marketing efforts are deploying virtual or augmented reality environments at a staggering pace and all these changes affect the data on which marketing is ever more reliant. 

In this cacophony of activity, it is often difficult to see where the line between IT and marketing should be drawn when it comes to data. The rapid pace and changing face of organizations simply mean that marketing and IT must come together to solve their challenges. 

In this environment, there are ways to ensure that marketing recognizes, develops and maintains a solid working relationship with IT. Here are five ways to get started. 

1. Seek first to understand

About 78% of IT people think they work collaboratively with marketing. The sad news is that only 58% of marketers agree that is the case. This 20% gap allows for a fair amount of disagreement and is often caused by a lack of understanding on both sides. 

One of the most important first steps in pulling together IT and marketing data is to understand the other side. Look at these practical things that can be done to learn more about the other organization. 

Dig deeper: What is marketing operations and who are MOps professionals?

Develop a one-day workshop for marketing and IT that talks through just the language of the two groups. For example, IT learns the acronyms in marketing (CMS, CTA, PPC, SEO). And marketing learns IT’s TLA (three-letter acronyms), such as OT, DNS, MWB, PGP. These are only the start. Both marketing and IT are very fond of jargon, including: 

Marketing:  

• CMS – Content management systems 
• CTA – Call to action 
• PPC – Pay per click 
• SEO – Search engine optimization 

IT: 

• OT – Operational technology 
• DNS – Doman name system 
• MWB – Malwarebytes 
• PGP – Pretty Good Privacy 

Also, marketing must explain the processes and tools that they use to the IT team, who in turn can explain how each of the tools works. This may sound like a waste of time for those already involved in IT and marketing operations, but often for the wider audiences, this is new, fascinating information. This also gives both teams the opportunity to display their stars by giving them key spotlights. 

Create an intern program internally between marketing data and IT where you exchange resources for a quarter. This may appear difficult, but it can be managed if the exchanges are well-selected from both sides. Sometimes picking a middle manager is the key to this exchange. 

Hold quarterly summits hosted by the CIO and the CMO. In these summits, highlight what the teams have achieved and how they are working together. Show off the data and how it has impacted both organizations. Make sure that both executives attend and they show their collaborative techniques through their interactions. Remember, the speed of the team is the speed of its leaders. 

Dig deeper: Driving marketing at scale: People, processes, platforms and programs

2. Don’t duplicate capabilities 

In some organizations that have not been functioning well, the tendency is to duplicate capabilities. For example, marketing will create an IT function to manage data or IT will segment their technologists by the area that they serve, making an IT marketing team. 

In these cases, marketing sometimes will argue that not all IT capabilities need to be in IT and there are some functions such as personalization development that marketing should have some control over. And the argument can have merit.  

However, the teams need to be coordinated, especially related to data management. Organizations need to have uniform control and governance over all their data and marketing and all other departments need to avoid duplicating the capabilities IT already has.  

If your company is organized into IT and marketing teams, you will need to begin the work of delineating roles and responsibilities between your marketing data technology team and IT. The most important decisions you will need to make are those associated with who swims in what lane.  

Marketers should remember that IT can provide a lot of value in developing databases and creating the infrastructure for managing data. IT’s expertise is developing databases and managing systems as well as helping automate marketing processes and providing triggers for key events.  

However, marketing often must engage new ways of going to market, faster than their IT team can respond. Take for example the rise of podcasts in the marketplace and the data generated from these events. Marketing may need that capability tomorrow and IT might have a problem with building it in that timeframe.

This situation often leads to marketing engaging outside agencies for the technology allowing them to move faster and react to trends. But, it may cause problems when trying to integrate data back into the company’s systems.  

If agencies must provide new capabilities, make sure that what they develop is supported by IT and that IT can pick up the maintenance of systems or processes if necessary.  

3. Build processes that allow time and space for collaboration    

This may sound like a simple action to take but consider that each department is doing its best to keep up with its own daily demands. Building time for collaboration isn’t usually on anyone’s calendar.    

Here are some examples of how you can create time and space for collaboration:  

Have a Monday morning stand-up session to align marketing and IT. Invite the marketing operations VP and the IT director for a 15-minute meeting to talk about what will happen during the week and the actions they are taking that will affect the other team members.  

Create teams of both IT and marketing that are dedicated to a single project. For example, dedicate two IT data specialists to the team with two marketing personalization specialists and give them the task of developing a system for personalizing customer interactions. Give the team visibility, highlight their progress and recognize their contributions both with IT and marketing.   

Build collaboration time into your annual planning. This sets the tone and pace for the two organizations and helps create environments that genuinely support both organizations. Also, don’t forget that collaboration really begins when each team member feels respected and engaged. The occasional “after work” gathering helps to generate camaraderie. Other little things make a big difference. For example, my favorite project with IT required two simple things: A special project room with a dilapidated couch and cupcakes every Friday. 

Consider where each party plays in your key processes. Your lead management process may be greatly helped by an IT specialist helping to manage the scoring of each lead ensuring that the lead is scored correctly and delivered to the most appropriate resource to follow up. IT may catch issues with systems that marketing specialists won’t.  

4. Create special processes and environments for data and analytics and sales 

Most often the tasks that fall between IT and marketing are those that deal with data. After all, data is the lifeblood of marketing. IT systems collect, secure and manage this lifeblood making marketing highly dependent on some aspects of IT. Because data is so important this is often the place where the fissure between IT and marketing is most evident. 

Marketing needs data about its customers — what they buy, when they buy it and which channel they use for purchases. Most likely, this information is not in the marketing systems that manage emails, events and the leads from these activities, but usually in an ERP or CRM system that is part of IT’s infrastructure consideration.

Managing these corporate systems puts IT under pressure to follow guidelines like the Sarbanes–Oxley Act for finance and the California Privacy Protection Act (CPPA) for data protection. 

Although marketing may be aware of these constraints, their major concern is communicating to customers and potential customers product information as clearly and rapidly as possible. This difference in approach to data has caused many marketing organizations to build their own data sources at the expense of millions of dollars.   

Because of this difference in operating model, some companies have found it beneficial to set up a chief data office. This office is often run by a professional that understands data and its usage. Although they may report to the chief information office, it probably has autonomy over the data that flows through systems for all functions in the organization.  

Increasingly, we are seeing this data function, coupled with analytics, as a major force in driving corporate growth. The best practice is to also include a marketing focus in the data office. This gives marketing the capability to use a host of corporate data and provides a structured IT capability.   

One of the areas that will still require marketing and IT collaboration is the acquisition of data to supplement and complement the corporate dataset. IT, responsible for the infrastructure, will have concerns about entering data from other sources into their systems. Marketing needs this capability to expand their understanding of their existing clientele as well as prospects. Having this function managed by a data office is important for both IT and marketing. 

Dig deeper: Why we care about data-driven marketing

5. Develop and manage joint goals and KPIs

We all know that we manage what we measure. But measurement for IT and marketing is often quite different and can result in misalignment between the organizations. 

Take, for example, the measurements for lead management. IT metrics will consider how rapidly the leads are moved from one stage of the cycle to another. Although marketing will want that information as well, they will also need to know the status of the lead when it makes that move. 

IT will be concerned with the systems managing leads and how they operate — their uptime, their throughput and their availability to users. Although marketing will appreciate these metrics, they will want additional information about lead quality, lead acceptance and lead potential value. 

Working together, IT and marketing can develop the right metrics to help manage each process. Developing a joint scorecard helps both teams better understand their impact on the other. 

Dig deeper: How to get the most value from your marketing metrics

IT and marketing work better together

IT and marketing are two quite different disciplines each moving at a different pace within the organization. 

Marketing’s job is to use data to constantly test new messages and ideas with customers — using that information in communications both internally and externally. 

IT has structured concepts and processes that they must live by to provide the consistent, high-performing infrastructure that a company needs. 

Both departments benefit from each other and together can be the powerhouse that generates growth for a company. 

The post 5 ways marketing and IT can work better together appeared first on MarTech.

In my last article, I shared the key areas for applying AI and ML models in marketing and how those models can help you innovate and meet client demands. Here I look at marketing’s responsibility for AI governance.

So, what is AI governance?

AI governance is what we call the framework or process that manages your use of AI. The goal of any AI governance effort is simple — mitigate the risks attached to using AI. To do this, organizations must establish a process for assessing the risks of AI-driven algorithms and their ethical usage.   

The stringency of the governance is highly dependent on industry. For example, deploying AI algorithms in a financial setting could have greater risks than deploying AI in manufacturing. The use of AI for assigning consumer credit scores needs more transparency and oversight than does an AI algorithm that distributes parts cost-effectively around a plant floor. 

To manage risk effectively, an AI governance program should look at three aspects of AI-driven applications:

• Data: What data is the algorithm using? Is the quality appropriate for the model? Do data scientists have access to the data needed? Will privacy be violated as part of the algorithm? (Although this is never intentional, some AI models could inadvertently expose sensitive information.) As data may change over time, it is necessary to consistently govern the data’s use in the AI/ML model.
• Algorithms. If the data has changed, does it alter the output of the algorithm? For example, if a model was created to predict which customers will purchase in the next month, the data will age with each passing week and affect the output of the model. Is the model still generating appropriate responses or actions? Because the most common AI model in marketing is machine learning, marketers need to watch for model drift. Model drift is any change in the model’s predictions. If the model predicts something today that is different from what it predicted yesterday, then the model is said to have “drifted.”
• Use. Have those that are using the AI model’s output been trained on how to use it? Are they monitoring outputs for variances or spurious results? This is especially important if the AI model is generating actions that marketing uses. Using the same example, does the model identify those customers who are most likely to purchase in the next month? If so, have you trained sales or support reps on how to handle customers who are likely to buy? Does your website “know” what to do with those customers when they visit? What marketing processes are affected as a result of this information?

How should it be structured and who should be involved?

AI governance can be structured in various ways with approaches that vary from highly controlled to self-monitored, which is highly dependent on the industry as well as the corporate culture in which it resides.  

To be able to direct to the model development as well as its validation and deployment, governance teams usually consist of both technical members who understand how the algorithms operate as well as leaders who understand why the models should work as they are planned. In addition, someone representing the internal audit function usually sits within the governance structure.  

No matter how AI governance is structured, the primary objective should be a highly collaborative team to ensure that AI algorithms, the data used by them and the processes that use the outcomes are managed so that the organization is compliant with all internal and external regulations.

Here is a sample AI Governance design for an organization taking a centralized approach, common in highly regulated industries like healthcare, finance, and telecommunications: 

What can marketers contribute to AI governance?

There are several reasons for marketing to be involved in the governance of AI models. All of these reasons relate to marketing’s mission. 

1. Advocating for customers. Marketing’s job is to ensure that customers have the information they need to purchase and continue purchasing, as well as to evangelize for the company’s offerings. Marketing is responsible for the customers’ experiences and with protecting the customers’ information. Because of these responsibilities, the marketing organization should be involved in any AI algorithm that uses customer information or with any algorithm that has an impact on customer satisfaction, purchase behavior or advocacy.  

2. Protecting the brand. One of marketing’s primary responsibilities is protecting the brand.  If AI models are being deployed in any way that might hurt the  brand image, marketing should step in. For example, if AI-generated credit worthiness scores are used to determine in advance which customers get the “family” discount, then marketing should be playing an important role in how that model is deployed. Marketing should be part of the team that decides whether the model will yield appropriate results or not. Marketing must always ask the question: “Will this situation change how our primary customers feel about doing business with us?”

3. Ensuring open communications. One of the most often neglected areas of AI/ML model development and deployment is the storytelling that is required to help others understand what the models should be doing. Transparency and explicability are the two most important traits of good, governed AI/ML modeling. Transparency means that the models that are created are fully understood by those creating them and those using them as well as managers and leaders of the organizations. Without being able to explain what the model does and how it does it to the internal business leaders, the AI Governance team runs the huge risk of also not being able to explain the model externally to government regulators, outside counsels, or stockholders. Communicating the “story” of what the model is doing and what it means to the business is marketing’s job.
   

4. Guarding marketing-deployed AI Models. Marketing should also be a big user of those AI/ML models that help determine which customers will purchase the most, which customers will remain customers the longest, and which of the most satisfied customers are likely to recommend you to other potential customers or indeed churn. In this role, marketing should have a seat at the AI Governance table to ensure that customer information is well managed, that bias does not enter the model and that privacy is maintained for the customer.  

Dig deeper: AI and machine learning in marketing: Are you deploying the right models? 

But first, get to know the basics

I would like to say that your organization’s AI Governance will welcome marketers to the table, but it never hurts to be prepared and to do your homework. Here are a few skills and capabilities to familiarize yourself with before getting started: 

• AI/ML understanding. You should understand what AI/ML are and how they work. This does not mean that you need a Ph.D. in data science, but it is a good idea to take an online course on what these capabilities are and what they do. It’s most important that you understand what impact should be expected from the models especially if they run the risk of exposing customer information or putting the organization at financial or brand risk.
• Data. You should be well-versed in what data is being used in the model, how it was collected and how and when it is updated. Selecting and curating the data for an AI model is the first place where bias can enter the algorithm. For example, if you are trying to analyze customer behavior around a specific product, you will usually need about three-quarters of data collected in the same way and curated so that you have complete as well as accurate information. If it’s marketing data that the algorithm will be using, then your role is even more important.
• Process. You should have a good understanding of the process in which the algorithm will be deployed. If you are sitting on the AI Governance team as a marketing representative and the AI algorithms being evaluated are for sales, then you should familiarize yourself with that process and how and where marketing may contribute to the process. Because this is an important skill to have if you serve on the AI Governance team, many marketing teams will appoint the marketing operations head as their representative.

No matter what role you play in AI Governance, remember how important it is. Ensuring that AI/ML is deployed responsibly in your organization is not only imperative, but also an ongoing process, requiring persistence and vigilance, as the models continue to learn from the data they use.  

The post Governing AI: What part should marketing play? appeared first on MarTech.

Customer expectations have never been higher. Amazon, Netflix and Google have set the standard for what customers have come to expect from technology and marketing. Amazon takes your order in one click and delivers it next day. Netflix wades through years of your entertainment choices and immediately suggests the next shows you’ll want to binge-watch. Google corrects your spelling, programs Alexa to tell you when that Amazon package is arriving and provides you with instant answers to the most obscure bar bets.  

AI Marketing, as shown in these examples, leverages technology to collect data, develop customer insights, anticipate next best actions, and make automated decisions about marketing efforts. If your goal as a marketer is to drive revenue, help lower costs through efficiencies, and drive customer engagement and satisfaction, AI Marketing can help you accomplish all of those things. 

Let’s explore three areas where AI Marketing can be helpful and what you should know about each area before starting any project. 

For each project, we’ll briefly explore what it is, how it works for marketing, and any pitfalls – technical or cultural — that you might need to be aware of in applying it.  

1. Marketing data management

What it is

Marketing data management is the process of collecting and handling marketing data, competitive intelligence and market research information. This function should not occur in the IT department – this is at the heart of what marketing does. Determining who the best buyer is for your product or service is clearly a marketing function. Collecting and managing the data associated with your buyers is marketing’s first consideration. What do you know about your customer? How many of them do you have? How do you describe a customer? Which ones buy which products or services? How large is the entire market for your product or services? All of these important marketing questions are answered through marketing data management.  

How it might work for you 

The use of AI and machine learning in this area can be applied both at the macro and micro levels. At the macro level, you can deploy AI and machine learning models to understand how your entire customer base segments into specific buying groups. At the micro level, you can predict a product’s lifetime value and associate it with individual customers. This micro-level data analysis helps you determine which customers or prospects are the best to pursue with which products. Accumulating data from these efforts only helps to make your models stronger and more accurate.

Accumulating data also requires that you manage the quality of the data you collect. Machine learning can be deployed against large datasets to deduplicate records or provide adjustments to standardize fields like zip codes or addresses. ML is also useful in helping to organize datasets for use in other AI applications.  

Other uses of machine learning include techniques like web scraping. This process is handy when trying to understand your competition. Each competitor’s website usually contains information that can be accumulated via this method such as new products available, customers mentioned and special programs. This is all public information, and with the right algorithms, data scientists can glean basic information about existing, as well as emerging, competitors. 

Dig deeper: Why we care about AI in marketing

Things to look out for

There are hordes of tools and consulting agencies in the market that want to help you with marketing data management. Tools include a wide range from Google Analytics to SAS, each providing a particular capability. Understanding what you want to accomplish – market segmentation, competitive analysis, etc. – will help you decide on tools or agencies that can support you. Getting your marketing operations lead involved is also a good idea.

When beginning marketing data management projects, consider first the purpose for managing your data and then look for the tools that are best in doing those identified tasks. When engaging consulting agencies, look for those that have experience in your area of need.  

2. Customer Intent

What it is

Customer intent data is sales and marketing information derived from observing the actions of the customer when accessing online content, looking at competitors, registering for events, contacting analysts, or engaging in any number of social media activities – from searching the web to posting on LinkedIn. Nearly every marketing organization today depends on this type of data to some degree, but it often doesn’t work for all marketers. 

How it might work for you

From the data collected about each customer’s interactions with your brand, website or staff, statisticians and data scientists can make inferences about the interests of the customer and their intentions to engage and purchase from your company. These inferences can be helpful in positioning to customers the right product at the right time. 

Once an algorithm is developed for identifying these customers, it’s imperative that you also gather input on the output of the AI model from the sales teams who will use this information, as well as from the marketers who might be applying it to online campaigns. Test the output of the model, but also test how sales and marketing is using it.   

Things to look out for

Sources of data are most important in determining intent. You already have good information about what your customers purchase, when they purchase, who they buy from and what type of company or individual is buying.  But intent data relies also on the actions that your customers or prospects may do before the actual purchase.

For example, this may require your AI algorithm to make connections between an inquiry on your competitor’s site and your prospect or customer list. There are firms that can provide contact-level intent data that identify an actual person taking an action. This information is helpful but must be used cautiously to avoid the “creepy” effect.   

Also, when using intent data, remember that it is only directional – it’s not specific or actual.  If your sales team uses intent data, they will need training on what the information actually means. For instance, Identifying a CIO who is likely to purchase an ERP system in the next 30 days may only mean that the CIO has begun a year-long process to identify a system. Giving this to sales as a lead without the explanation could be a blow to your marketing organization.

3. Opportunity and purchase prediction 

What it is

Forecasting is a way of predicting what will happen in the future. For example, you can forecast what the sales of products and services may be in any given period.

Sales forecasting helps management plan for expenses, business growth or economic downturns. It’s the crystal ball that sales managers use when predicting whether they will make their targets or not. Sales forecasting is usually fairly accurate because it uses past sales transactions to predict future ones.  

How it might work for you

Marketing can use predictions in their work as well. For example, Norway’s tourist department uses AI methodologies to predict how many tourists will visit the country. Although not a sales figure, it is an important KPI for Norway tourism. AI or advanced statistical analysis can also help predict attendance at events, numbers of people who will take you up on a special offer made on your website, or the number of qualified leads that will make it through to purchase. 

Things to look out for

Forecasting can be very rewarding, but it is only useful if it proves to be accurate. Here are a few tips:  

• Consider more than just last quarter’s numbers. Good sales forecasting has at least 18 to 24 months of company performance data.  Working with that much data allows you to be more precise in your forecasts.  If the data is not available, avoid forecasting.
• Account for change in your overall business. Good forecasting accounts for the sale of the same product and service over time. Acquiring new products to sell, divesting of products and changing pricing or strategy all effect your ability to accurately forecast sales. Also, if you are predicting other marketing events, one of the variables that is often important is the budget allotted for an activity. If that varies greatly from quarter to quarter or year to year, then it may be more difficult to forecast, or you may need to allow for these variances in the model.
• Don’t try to forecast sales into new markets with new customers. No matter how tempting it may be, you need performance data to forecast sales. Leave this forecasting to your sales teams. This is often considered business development, and these sales teams know how to evaluate whether a customer will purchase or not.  For marketers, this is a matter of collecting the information from the sales team, developing a profile of a good customer and then applying “look alike” analyses to other prospects.

These are only a few of the key areas of marketing for applying AI and machine learning techniques. As you explore more in this world, you will find that opportunities abound especially in helping marketing to streamline the myriad decisions they make each day.

The post AI and machine learning in marketing: Are you deploying the right models?  appeared first on MarTech.

One marketing team reads the report and then usually convince themselves that the data is wrong because their campaigns are working. What other reason could there possibly be for this quarter’s 10% growth in revenue?

The other marketing team devours the report by noting trends in web interactions, analyzing attendees’ reactions to their last podcast and checking the number of sales-closed opportunities. Their revenue is also growing by 10%.

Then, something happens (merger, stock market decline, pandemic, war – fill in the blank). Which team do you think recovers the quickest by adjusting their marketing plans?

That’s an easy question. Of course, it’s the team analyzing their data every week. They will know which market segments will be most impacted by the “event” and what tactics will need to be adjusted. They will see declines or movements in their trends, allowing them to validate their tactic changes and fluctuations in results with data. 

These five steps help ensure that your data provides you with actionable information and enables you to guide your marketing efforts. 

1. Start with the basics

Before you dive into your weekly or monthly marketing report, here’s what to consider as you become a better champion of reporting and analytics.

• Source:  Know where you are getting your data. Your data source should include your transactions for clicks, searches, website registrations and sales interactions. A CRM system should give you numbers for marketing produced or sales accepted opportunities, closed marketing opportunities and revenue generated by account. In addition, other sources of data from SEO vendors, intent data vendors and industry-specific data suppliers should also be noted on your reports, and you should know how this data affects the numbers.
• Quality:  Know what the quality of your data is at the source. The IT or data organization should provide insight into the quality of the data used, at least monthly. This is especially important if you are doing deep-dive analytics or predictions on the information. For example, if you are trying to predict which products will be selling the best in the next quarter, you will have to be assured that you have at least a year or two of good, complete and accurate data. Prediction needs solid information to predict with higher accuracy. Asking these questions of the reporting team helps:
  • How many duplicates are in the data this month?
  • How has the accuracy improved or declined?
  • Any anomalies occurred that would prevent consistent trending? Anomalies such as product introduction, new data feeds and system changes should be noted and explored.
• Timing:  Know when the data was pulled into your report. This often requires understanding the entire process, from data entry to reporting. For example, if you are collecting email addresses and permissions in one area of your business, but it takes two days to collate and make that data available in the reporting system, then you may be missing information at a crucial time. Since weekly marketing reports are primarily on what HAS happened, you need to make sure that they reflect the happenings accurately. It’s just a best practice to put a date on the data itself and the report, especially if your systems have lags in the automated processes.

2. Anchor on the revenue

Both teams have the right idea about what really counts in marketing – sales. They are both looking at the company’s revenue because that is usually an overall corporate goal. But just because the revenue is on track doesn’t mean that marketing had anything to do with it. The sales team will be the first to point that out. Revenue should be the North Star for all marketers. 

Analyzing where revenue comes from is one of the most important tasks of marketers. This requires some deep-dive analysis of the buyers and their transactions.

Once at a large, high-tech computer company, my team was asked to segment different products. The overall corporate belief was that small and medium businesses did not buy our high-end products. By analyzing the data, we discovered that SMBs did indeed buy our high-end products, but not as often and not directly from us. Because they needed support in implementing the products, they purchased them from a reseller or systems integrator (SI) at a premium. The two-tier distribution hid from us the fact that they were purchasing. Resellers and SIs, at the time, were not required to share end-user information.

Looking at your overall revenue and where it comes from can help you fine-tune your marketing engine. Look at regions and how they are performing. Look at products – which are moving and which are not. Evaluate purchases within industries or departments within companies. 

Revenue is the goal of all marketing efforts, but many organizations struggle to link marketing to the revenue generated. A close working relationship with sales helps drive this connection.

3. Let trending tell you where you’re headed

Evaluating your marketing data over time is very important because it’s a sad fact that marketers tire of their marketing message long before our customers do. Today’s digital world adds another dimension to this, however. Marketers can often see in real-time what a customer’s reaction might be to a given message. This lulls marketers into believing that all messaging is direct and instantaneous. But unless the customer purchases because of the message and you can measure that purchase, you shouldn’t just abandon your message or strategic direction.

That means that our messaging may not immediately affect and cause a massive purchase of our products just after we drop our first campaign. It’s likely after 5 to 10 campaigns of the same message and intent – you see upward trends as your message is received, absorbed, and acted on. Therefore, looking at marketing trending is so important. 

Trends can tell you if your customers are picking up on your messaging and purchasing your products or services, but they can tell you about the pace at which this is happening. This is often a good signal for marketing managers to relay to sales or service teams, to tell them when they might expect an influx of inquiries, sales, etc.

4. Evaluate tactics as a whole

Integrated marketing means your messaging and how you distribute the message are coordinated. You have many tactics: email, events, SEO, website clicks, etc. And each of these can have a multitude of additional metrics. For email alone, you can have delivery rate, open rate, click to open rate, conversion, bounces, spam complaints, etc. Although these metrics may aid in helping you to manage your efficiency, they do not always help with marketing effectiveness.

HubSpot has a great template for evaluating marketing tactics. It looks at numbers for reach across all your social sites such as Twitter, Instagram and LinkedIn, number of visitors to your sites, leads generated, customers and conversion rates for tactics. Looking at tactics as a whole can give you a much better picture of the effect of your overall efforts.

5. Pay close attention to your customer’s experience.

Fortunately, today we have systems that allow us to measure the social effect of our marketing. In other words, what are your customers’ feelings about your product or service? This data is often overlooked in marketing reporting and analytics because it is “fuzzy data” and sometimes hard to manage or interpret. However, this information is crucial. 

Take, for example, Dell. This large technology company has built a state-of-the-art customer feedback center in India that monitors customer feedback constantly across the entire web. They can almost instantly tell how new products or services are being perceived and where their biggest issues are across the globe. Customer comments are aggregated for actions that are sent immediately to service representatives. 

Customers are talking about your products every day. If you don’t have a way to hear those comments, you are disadvantaging your company.

Take action

The greatest lesson to take from this look at marketing reporting is that knowledge is only transformed into wisdom through use. Being a marketing expert means that you understand your metrics and the data behind them. Many marketers get lost in the number of metrics that they record. That’s where we go back to the goals and make sure that all our metrics are closely tied to the company’s goals and marketing goals. Ultimately, your reporting needs to roll up to an executive, the person most likely to be evaluating marketing overall. 

Dig deeper: How to choose a marketing analytics platform

Laura Patterson, CEO of Visionedge Marketing, a marketing metrics company, says that at the executive level, you need only a handful of core measurements that fall into these categories:

1. Marketing contribution to sales
2. Customer movement – acquisition, retention, value
3. Efficiency improvements
4. Bottom line: Marketing’s financial contribution and ROI

Carefully understanding your data, evaluating the metrics you establish for marketing, communicating the effects of those metrics and taking actions guided by them helps you as a marketer gain a strong reputation as a contributor to your company’s growth.

The post 5 steps to make the most of your reporting and analytics appeared first on MarTech.