понедельник, 21 июля 2025 г.

Management Functions and Organisational Processes. Block-3 Managerial Processes-II. Unit 9 - Decision Making

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Mapping AI-Based Martech Using the STIB Innovation Diffusion Model

This post fleshes out the STIB model of innovation diffusion, tests it by classifying recent customer management product announcements into STIB categories, and draws some insights about the model and customer management trends. As a bonus, it proposes a future of "hyper-personalized, financially-optimized treatment of each customer during each interaction across all channels."

The Substitution-Transformation-Infrastructure-Business Model (STIB) model of innovation diffusion that I presented in my last blog post is intriguing, but does it reflect reality? One measure is whether it provides a useful classification of industry products. To test this, I looked at the past ten weeks of product news in the CDP Institute Daily Newsletter, which came to about 50 items. Results were quite interesting.

But before we get to that, let’s flesh out the model a bit. It describes two core elements:

Innovation: the development whose impact is being measured. This might be electric motors or internal combustion engines. (More formally, innovation is a change in technology, where technology is defined as the tools, methods and knowledge used to perform a task. "Technology" in this sense isn't always "technical": a new marketing methods and financial tools can be important innovations. But I digress.)

Application: the process or product changed by the innovation being studied. This might be manufacturing in factories (a process) or transportation vehicles (a product). The application is assembled from components, which might be physical objects in a product or steps in a process workflow. The same innovation might affect many different applications but it's often more useful to study just one.

The model describes how an application changes in response to an innovation. This change comes in three stages:

Substitution: the innovation is deployed as an exact replacement for one component in the applications, without changing anything else. Electric motors were hooked to turn factory shaftwork instead of water wheels; internal combustion engines replaced horses for powering carriages.

Intermediate States: this is a period of experimentation. Multiple components are changed as the industry explores how to best take advantage of the innovation. The innovation itself continues to mature during this period, opening up additional possibilities.

Transformation: the industry settles on an optimal design incorporating the innovation. This "final" design is fundamentally stable although incremental improvements continue. Factory tools incorporate built-in electric motors; motor vehicles locate the engine and drive train most efficiently.

The model also tracks changes over time to technologies beyond the application itself:

Infrastructure: this includes products, suppliers, and processes that support the transformed application or are changed as a result. Electric motors require generating stations, power grids, and new machine tools, while they result in more efficient factory designs. Parts and materials manufacturers support motor-driven vehicles while auto ownership spawns new roads, filling stations, employment opportunities, legal frameworks, and more.

Business model: this describes business methods such as revenue sources, pricing models, marketing, distribution, customer service, funding, and ownership models. Larger factories lead to scale economies that favor large, national brands. Complex auto technology favors large organizations with deep resources for research, mass production, national advertising, and dealer networks.

The graphic illustrates relationships among these elements.*

In the initial state, the application, infrastructure and business model are all in their baseline (i.e., pre-innovation) configuration.

This is followed by substitution, where the innovation is applied to make a slight change in the application while infrastructure and business model are unaffected.

In the third stage, intermediate states, developers try variety of changes to the application and the infrastructure starts to mature in response to growing demands. Some will be a dead end (direct current electrical systems, steam powered cars). Others may hit on the transformed design before infrastructure is available to support them. Vendors may also fail if they don't change quickly enough to keep up with competition.

In the final stage, developers converge on a transformed configuration and application change comes more slowly. Some companies drop out or merge as the market consolidates. The infrastructure continues to evolve and new business models start to emerge.

The Future of Customer Management

Applying the STIB model requires specifying the application being changed and the innovation that is changing it. This might be tricky in some situations, but it’s pretty simple in the current context: the application is customer management while the innovation is artificial intelligence.

What’s not simple is predicting the form of the transformed application. Hindsight about factory equipment or auto design is easy, but we don’t know how customers will be managed in the future. This prediction isn't strictly necessary: we can simply observe changes as they occur without assessing their relation to the final state. But that assessment helps to organize the analysis and gives buyers and developers decide where to invest their resources.

Predicting the transformed state is a thought experiment: identify the constraints imposed by the current technology and imagine what the ideal design would look like if those constraints were removed (and replaced by whatever constraints the innovation imposes.)

As I see it, the dominant feature of customer management today is the number of separate steps that are performed by different people. There’s a creative team with a multi-step process to develop content, a targeting team with a multi-step process to select audience segments, a media team with a process to buy advertising, and multiple operations teams with multi-step processes to deliver messages via websites, email, social media, connected TV, digital video, games, out-of-home, podcasts, and elsewhere. The reason we have so many teams with so many steps is the limited capability of individual humans: each can be expert in only a narrow area, so many must work together to deliver a complete result. And, because they’re human, each person can only deliver a relatively small number of outputs over time. Thus, each output must apply to many people to deliver messages to everyone.

AI removes this limit. At least in theory, a single AI agent can expertly execute all steps in the messaging process, combining content creation, audience selection, media buying, and delivery. In practice, a single super-agent is less likely than a master agent that calls on specialized subordinate agents. But so long as those agents complete their work almost instantaneously, the process will still function as a single step. Ideally, these agents would view all available data and collaborate with each other: content development would be informed by audience characteristics; media buying would take into consideration other channel opportunities; and so on. All these decisions would be coordinated to produce the highest total value: for a given advertising impression, the system(s) might simulate the results of several different creative treatments for several different offers for several different products, and then select the best creative/offer/product combination – or conclude that even the highest possible value for that impression is too low to justify the investment. This requires a sophisticated value prediction algorithm which would consider long-term as well as immediate results.

In addition to speed and coordination, the process would be completed at near-zero incremental cost.

In short, I see the transformed state as hyper-personalized, financially-optimized treatment of each customer during each interaction across all channels.

This is a radical change. (That's why they call it a transformation.) There are no more campaigns (defined as a predefined sequence of messages), no more audiences (defined as a group of customers who receive the same messages), no more content (defined as a fixed combination of text, images, and offers), and no more media buys (defined as group of impressions purchased together). What we have instead is a master agent orchestrating customer interactions across all channels to optimize use of budgets, data, customer attention, products, staff, and computing power. That agent would call internal and external data to guide its actions and would deliver messages across both internal (owned) and external (paid) media.

In the transformed world, today's creative, analytics, media, and operations departments have largely vanished, apart from a few human(?) experts left behind to monitor the AI. On the other hand, there might be more need for humans to do strategy and product development.

This vision also points to new infrastructure and business models:

Supporting infrastructure would include vendors building the base AI systems, which are too complicated for most companies to build for themselves (although non-technical users might use no/low-code tools to tune them); data tools to prepare, integrate, and expose data from all sources in real time; and analytical tools to measure interaction value.

Surrounding infrastructure would include new media that provide real-time access to their customers; low-friction integration methods to connect marketers with these systems; new billing and analytical methods; and social/legal frameworks to govern customer data collection, sharing and privacy.

New business models would be needed for companies and the systems, data, and media providers that support and surround them. With each interaction managed separately, providers might become on-demand services that charge for value provided in each case rather than billing based on labor, system use, or impressions.

I’m not sure this will happen. At most, I’d bet one of my later-born children on it. I’m laying it here out because the STIB model works best if you measure against a transformed state (and because it’s fun to think about.)

Mapping against the STIB Framework

That said, let’s try mapping recent product news against the STIB framework.

Application

The application we’re analyzing is customer management, which is roughly what we cover in the CDP Institute daily newsletter. This gives us a reasonable collection of announcements to work with, although it’s just a small sample based on items that happened to appear during a relatively brief period.

If the STIB model is correct, we should find clusters of products on a spectrum from business as usual, to substitution of AI within current processes, to complete transformation. Because transformation doesn’t happen all at once, we would expect to find several intermediate stages. Indeed, that’s the case.

Current State: The first cluster would hold products that execute the existing process without any change. AI-powered co-pilots might fit here. But co-pilots are so common that we don’t bother to write about them in the newsletter. Nor do we usually cover products that aren’t doing anything new. So this cluster is empty apart from one item about agentic helpers.

CommerceIQ Offers Agentic Teammates to Ecommerce Marketers

Substitution: The second cluster is simple substitution: products that replace a discrete task with an AI-generated equivalent. We see plenty of these, often offered as collections of (separate) AI tools for multiple tasks. We also see toolkits for users to build their own. So long as each AI tool executes one task separately from the others, this is still substitution. Recent examples are:

Intermediate Products: Now we move into products that change the underlying process but don’t reach the fully transformed state. The news items seem to fall into three clusters. It’s important to note that we didn’t define these in advance: they have emerged from the data itself.

Content Creation: This is a popular task to automate. It’s a single task but more than substitution because most vendors connect their content generator to response data and use this to automatically optimize content over time.

Goal-Driven Workflow: this cluster holds systems that have automated development and execution of a multi-step workflow, such as audience segmentation, journey design, or media buying. Like content creation, these usually let users specify a goal for the workflow and collect data to help measure results.

Customer Management: these products collect data to support optimization, help users to create messages, and select and deliver messages across customer touchpoints. They come the closest to the fully transformed state but don’t support all channels or create hyper-personalized messages in real time.

Transformed Products: This cluster would hold products that deliver the fully transformed process. There’s a good chance that some vendors have this mind, but we haven’t seen any products that deliver it. So the cluster is empty.

Infrastructure

Infrastructure and Business Models won’t take their final shapes until the transformed process is fully deployed, but they do co-evolve with the application changes. This applies especially to supporting infrastructure, parts of which must be in place for some intermediate product to function effectively.

Supporting infrastructures: The key supporting infrastructures for transformed customer management are inputs (data access and quality), internal processes (agent cooperation and analytics), and outputs (media integration). We see announcements in all these areas.

Data Access: most data access announcements describe accessing data in real time without loading it into a separate database. So far, these developments focus primarily on reading data in the company’s internal systems. But remember that the full vision for transformation includes access to third-party data such as compiled customer behaviors and local weather. We do see some of that, although none is in the current sample.

Data Quality: these tools prepare data for AI use. Many data quality vendors have added features to support AI use and have added AI-powered offerings. These apply to customer data but we don’t usually write about them, so this cluster is fairly sparse.

Agent Deployment: this describes technology for building agents and helping agents work together. It’s another field with extensive activity that is largely beyond the scope of the daily newsletter. It will be critically important if the transformed state involves teams of agents that cooperate closely with each other.

Analytics: any goal-seeking agent will need internal analytics to guide its decisions. Still, there may turn out to be a market for independent agents that make their results available as a shared resource for teams of specialist agents. This would apply especially to customer value analytics, which are needed to compare opportunities across different channels.

Hightouch Adds Journey Simulations

Media Integration: this is the output infrastructure that deliver messages created by the hyper-personalization. The tools are steadily encompassing more channels.

Surrounding Infrastructures: changes to the surrounding infrastructure happen after the transformed application is in place. This makes them harder to predict than supporting infrastructure, which develops sooner. We do have some current developments that are likely to become more important as hyper-personalization matures. There will no doubt be others.

Integrated Commerce (Sales Agents): this describes shopping in non-traditional channels, including retail media, search engines, social media, video, connected TV, and mobile apps. These can work without hyper-personalization but are vastly more effective when offers are tailored to the individual and context. In many cases, the interface will be a chat-style, AI-based sales agent that has a real-time dialog with the customer.

AI-Based Search (Buyer Agents): this describes marketing that is targeted at AI agents rather than humans. Today's most common implementation is AI search overviews, which do research for buyers. This changes the goal of search marketing from attracting traffic to appearing in genAI summaries. Other agents are evolving for other types of research and other stages in the sales cycle including making purchases on the customer's behalf.

Interacting with these buyer agents at scale and cost-effectively will require AI-based sales agents. In some ways, it won’t matter whether the sales agent is interacting with a person or a bot: to the sales agent, both are collections of data that must be analyzed and responded to appropriately. That said, the behaviors of humans and bots will be significantly different, so the sales bots will no doubt develop separate approaches to each. Ultimately, marketing systems may give buyer agents direct access to (some of) the product and promotional information they provide to sales agents, bypassing the sales agents altogether.

Because this is a relatively new development, our news coverage includes more research reports than product announcements.

Business Models

Few companies make announcements about changes in their business model, especially when those changes involve firing people. As a result, we have few newsletter items on AI-based business models. One change we do see is movement towards pricing based on value created rather than resources consumed. The transformed process will surely create other opportunities, perhaps including “no staff” companies run almost entirely by AI or “no product” companies that source products in real time as they find interested customers. It's likely the new business models will include ones we haven't even imagined.

What We’ve Learned

The main purpose of this blog post is to determine whether the STIB model provides a useful way to think about technology innovations. Of course I’m biased, but I believe it does. Not only was it reasonably easy to slot products into different categories, but the process generated several helpful insights:

Products that substitute AI for one step within an existing workflow are significantly different from products change the workflow.

It helps to envision the final, fully transformed product so we can assess intermediate products, identify the supporting capabilities and infrastructures it requires, and predict the surrounding infrastructure and business models it is likely to generate.

We should acknowledge that our prediction could be wrong. It may help to build scenarios around alternative outcomes.

Intermediate products appear before the final transformation. These become possible as new capabilities, infrastructures and business models appear. Observing the intermediate products helps to assess whether the changes are moving in the direction we expect or to adjust our prediction of the transformed state..

A minimum set of capabilities, infrastructures, and business models must be in place before the final design can succeed. Companies will fail if they offer the transformed design before supporting infrastructures are in place.

Capabilities, infrastructure, and business models continue to evolve after the (successful) transformed design is introduced. These developments make the product more effective and exploit the opportunities it creates.

Continued evolution creates rapid growth and expands the value of the final design, giving it an increasing advantage over alternatives. This advantage ultimately locks other configurations out of the market, even though some may be technically superior.

Applying the STIB model to customer management offers additional insights. Our sample of product news is enough to show:

Many current products do simple substitution. These are the easiest to deploy and can show immediate improvements in cost and quality. (There are also many products that support the existing workflow without any changes, but those don’t show up in our sample.)

Some intermediate products are already available. Most of these automate a single stream of tasks within the customer management workflow, such as content creation, campaign design, media buying or analytics.

Intermediate products usually work within a single department. This makes them easier to drop into the larger workflow and reduces the number of users whose work is disrupted.

Some products aim to automate an entire workflow, such as campaign design, development, and execution. Doing this with autonomous AI agents is the leading edge of the industry today.

Some companies offer components that support the final vision. These include application capabilities such as content optimization, response simulation, and advanced attribution, as well as infrastructure and business model changes including agent coordination, cross-company data sharing, touchpoint integration, and performance-based pricing.

I haven’t seen any products that promise the predicted final state of “real-time, hyper-personalized, omni-channel messages.” This has surely occurred to many smart people, so I’ll guess they have decided (correctly) that the capabilities, infrastructure and business models aren’t ready yet.

The final state may be delivered by a single integrated product or by multiple agents working in concert. Remember that the final state requires close cooperation between advertisers and media companies and between different departments within the same company. This makes a multi-agent solution more likely and reinforces the needed for data- and process-sharing technology..

Implications

Here are some practical implications of what we’ve discussed.

Substitution products can be valuable, but the benefit won’t last. It’s tempting to argue that substitution is a poor investment because it offers only incremental improvements on today’s current processes and that companies, and software developers should instead focus on more profound solutions. But the plain fact is that substitution creates substantial benefits and is easier to deploy than changes that require process change. Product developers and business users shouldn’t shy away from substitution but they do need to recognize it has a relatively short shelf life. Product developers should also realize that nearly all incumbent vendors will add substitutions to their current products or have already done this. That makes it hard to convince users to change to a new system on the basis of substitution alone. Attracting new clients will require solutions with more substantial advantages, which is what the intermediate product designs can offer.
The goal of "real-time, hyper-personalized, omni-channel messaging" is not yet widely discussed. What leads me to expect this as the transformed state is the growth of direct sales in social media, search results, connected TV, podcasts, and pretty much every other channel. This "instant commerce" collapses the multi-step "awareness, interest, desire, act" cycle into a single moment when the customer is presented with an opportunity to buy. Taking full advantage of this moment requires marketers to connect with all message opportunities, to gather all data so they can assess the potential value of each opportunity, and to deliver the most effective possible message for each opportunity they purchase. Only AI can do this effectively at scale, most likely by presenting intelligent agents to interact with customers who engage.**
The transition can be gradual. Instant commerce can be deployed in one channel at a time, can work with limited data, and doesn’t require advanced message optimization. Companies and vendors can move towards the fully transformed state in stages, building experience, product, infrastructure, and business models along the way.
Instant commerce depends on long-term relationships. That may seem like a paradox but customers will only engage with companies that they trust. There’s no time to build trust during the interaction moment, so trust must be built in advance. The good news is that the importance of trust is widely recognized and the methods for building trust (among humans) are well understood, if not always well executed. The industry may need new lessons in building trust among AI agents.
Infrastructure may offer the greatest opportunities. The biggest gaps between what’s currently available and what’s needed in the transformed state seem to be deep connections to collect external data and interact with touchpoint systems.

Shallow connections already exist, but real-time, hyper-personalized, omni-channel messaging implies real-time queries of external data sources about individual customers to collect up-to-moment information on behaviors. The touchpoints where those behaviors occur must capture, identify, assess, expose, and charge for that data in real time in a privacy-compliant fashion. Bear in mind I’m talking about touchpoints outside the company that wants to use this data. Very little technology exists to do that today. Data clean rooms are a start.
Beyond sharing information, those same touchpoints need to receive ad messages and deliver them to their visitors, again in real time and with feedback on response. Most of the messaging technology will reside with the ad buyer or middlemen, who will need to receive notification of contact opportunities, gather data and assess those opportunities, select the appropriate message, bid on delivering the message, transmit the message on bids they win, and measure results. Again, the existing technology to do all this in real time, at scale, and across many channels is limited at best. (Today’s programmatic ad system is a partial model.) In addition to data movement, this process requires sophisticated evaluation models so marketers can accurately bid on the projected value of each interaction.
Because all this interaction is happening between different companies, the infrastructure technology must be widely shared. This could imply broadly accepted standards implemented by many developers, or, more likely, proprietary technology built and sold by a few major suppliers. Competition to be one of those suppliers will be fierce but the rewards are likely to be huge. The rewards might diminish over time once the process is well enough understood to create open standards that are a viable, cheaper alternative.

Data quality doesn’t get the attention it deserves. Survey after survey shows that data issues are the top roadblocks to marketing, personalization, and AI success. Yet companies rarely make data quality an investment priority. There isn’t much to say about this except that real-time, hyper-personalized, omni-channel messages make data quality more important than ever. This is yet another piece of infrastructure that’s ripe for improvement.
Buyer bots might change everything. Buyers have limited attention, which is why gaining attention has always been the first step in successful marketing. This still applies to real-time, hyper-personalized, omni-channel messages -- so long as they're being sent to humans. But if customers delegate their purchasing activities to bots, the fundamental truth is no longer true: buyer attention will no longer be limited. Developments such as search engine optimization and AI search overviews as early examples of marketing to bots: marketers must target the algorithms, not attract human attention. But search marketing of any kind is still aimed at putting messages in front of eyeballs. Truly automated purchasing will remove humans from the entire process. The change won't happen overnight but it seems plausible to expect a mix of human and bot buyers in the near future. A STIB model with bot buyers as the final transformed state would be quite different from the one I’ve presented here.

Summary

This post has explored whether current developments in customer management technology can be effectively analyzed with the STIB model of innovation diffusion and whether the results provide useful insights into industry trends. I believe the answers are Yes and Yes. I've also presented a specific vision for the industry future, of "instant commerce" delivered through real-time, hyper-personalized, omni-channel messages. I can't promise that this is correct, but it's an interesting starting point for discussion.

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* The graphic is conceptual but could be quantified by counting the number of components in each product that match the final transformed design or that differ from the initial state. Fun!

** Things are admittedly a bit more complicated for non-impulse purchases. But I’d argue you’re still trying to motivate an action in the moment, even if it’s only saving an offer in a wallet for future consideration.

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воскресенье, 20 июля 2025 г.

Automating B2B Marketing

Three forces are shaping today's B2B marketing landscape - the growing power of B2B buyers, the need to make marketing messages and materials relevant to potential buyers, and the recent emergence of technology tools that automate many marketing tasks. I've covered buyer empowerment and the importance of relevant marketing communications in previous posts. This post will focus on marketing automation technologies.

B2B marketing automation systems - also called demand generation systems - are software tools that are designed to help marketers acquire, nurture, qualify, and distribute leads to sales. Demand generation systems automate four types of B2B marketing tasks.

Lead Generation - All demand generation systems enable users to create and execute lead generation e-mail campaigns. Demand generation systems can also host landing pages and the forms that are used to capture campaign responses, and they can use cookies to track visits to Web pages at a company's main Website in addition to the campaign landing page(s). Support for channels other than e-mail and Web pages is inconsistent. For example, if a lead generation campaign involves direct mail, the direct mail component must usually be managed outside the demand generation system.

Lead Nurturing - Lead nurturing is the process of communicating with prospects on a regular basis until they are ready to buy. For example, a lead nurturing program might involve sending a prospect a particular sequence of e-mails on a specified schedule. Demand generation systems automate the execution of lead nurturing programs. Automated lead nurturing is probably the most important feature of demand generation systems because nurturing programs are difficult to implement without automation.

Lead Scoring - Lead scoring is a method of qualifying prospects by assigning numerical "points" based on information provided by the prospect and on the prospect's behavior (e-mails opened, white papers downloaded, Webinars attended, etc.). All demand generation systems allow users to define scoring criteria and assign scoring values to those criteria.

Lead Distribution - When a prospect's lead score reaches a pre-determined value, the lead is deemed to be sales ready, and the demand generation system passes the lead to sales. Other events can also be used to trigger a hand-off to sales. Demand generation systems are usually configured to distribute leads to sales automatically when these triggering events occur. Most, if not all, demand generation systems offer integration with salesforce.com, and some vendors offer integration with other sales automation and CRM products. This integration makes distributing leads virtually seamless.

Forrester Research has estimated that only 2 to 5 percent of B2B companies have implemented demand generation systems. My take is that this market is on the cusp of a huge growth spurt. I believe this growth will occur for three reasons. First, demand generation systems exist for virtually all sizes of B2B companies. Monthly costs start as low as $200. Second, all of the major demand generation systems are sold as a hosted solution, which means that companies don't need extensive IT resources to implement and use them. And finally, there is a growing body of evidence from early adopters that demand generation systems can significantly improve marketing and sales performance.

If you are a corporate marketer and you haven't already invested in a demand generation system, you should start looking at these technologies now. If you are a marketing services firm, you need to be thinking about how you can help your clients leverage the capabilities of demand generation technologies.

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7 Ways B2B Marketing Automation Could Change Your Business

By Tony Shannon

Marketing automation is pushing its way into virtually every aspect of digital marketing. It has a reputation for being an extremely valuable tool to help customers generate, manage, and close new customers that enter their lead pipeline.

Investing in B2B marketing automation consulting can have a huge impact on small to mid-sized businesses, changing the way that they do business overnight.

For those that are new to marketing automation, it can be difficult to understand the scope of marketing automation and the benefits it can provide.

It sounds complicated, but there are many systems available today that can be easily installed and managed.

It's figuring out which solutions will work well together that is the hard part. Marketing automation has a lot of practical uses that can handle the mundane tasks that businesses often handle by hand every day.

Top 7 B2B Marketing Automation Examples

Before we dive into B2B marketing automation examples, let’s go over what marketing automation is, and why it could be just the tool your business needs to get ahead of the competition.

B2B Marketing Automation Solutions

Marketing automation is simply a way for you to take tasks that otherwise would have to be handled by hand and automate them using software and digital tools.

In most cases, the things that B2B marketing automation is able to handle are tasks that otherwise would have gone overlooked or happened at a much slower pace if they had to be handled by a live person.

Where marketing automation really shines is in the ability to generate and nurture leads through the sales funnel.

Using automated messaging and other tactics, you can help to build relationships with prospects over time, helping them to get to know company while positioning your products as a solution to their problems.

Marketing automation is used in many digital marketing channels. While most will think of email marketing when they think of marketing automation, you can automate marketing in nearly any channel.

With the help of B2B marketing automation consulting you can save your business time and money while growing your revenue.

Let’s take a look at some of the different marketing automation strategies that you can use to bolster your business:

Example #1: Automated Website Live Chat

A live website chat can be a great way to capture leads that would otherwise be lost if they didn’t have the option to ask questions and receive immediate answers.

The problem is that you have to employ someone to watch your live chat throughout the day and interact with customers.

For many, live chat has become an expectation. A Forrester research piece found that 30 percent of customers expect live chat today.

For an already busy customer support team, this can really take up their time and make it difficult to attend to their daily duties.

However, there are solutions available today that allow you to reap the benefits of a live chat without having to hire someone to handle it or take time away from your current customer service reps.

Tools like Collect.chat give your customers an automated way to interact with your company, connect with customer support reps (when their questions warrant it), and learn more about your business and product.

Example #2: Action-Triggered Sales Notifications & Robust Sales Tracking

Certain pages on your website will be typically visited by leads that have a high interest in your product.

These pages might include your price page, terms and conditions, or very detailed product-focused content that you have published.

Visits to these pages by your leads can be an indication that they may be ready to buy. Failing to contact them at this time could be a missed opportunity.

Using email to send sales materials to leads that visit these pages can help you to identify buy-ready prospects and capitalize on those opportunities.

Example #3: New Customer On-boarding

For SaaS companies, one of the most difficult tasks that you have is getting your trial and new customers to learn your software and integrate it into their business processes.

Using marketing automation, you can send a sequence of automated emails to your customers that walk them through using your product and provide them with genuinely valuable advice for getting the most out of their investment.

This gives companies a hands-off way to complete this task without having to dedicate time to ongoing product demos and hand holding with new customers unless they specifically ask for help.

Example #4: Lead Nurturing & Email Marketing Software

Did you know that 96 percent of visitors that come to your website aren’t ready to buy? When a new lead enters your system, what steps do you take to ensure that they become a customer?

For companies that handle this process by hand, this can usually mean a lot of email correspondence and multiple phone calls.

However, this process can be handled mostly through automated email correspondence and other tactics, only handing sales qualified leads off to your team when they are truly ready.

Marketing automation uses email sequences to inform and educate your leads, building rapport with them while delivering value.

As you build your relationship with them, you begin to sprinkle in more sales-focused materials, walking them toward buying your product.

Example #5: Churn Reduction & Service Analytics

For SaaS companies or any company with a monthly subscription for their product, churn is a serious issue.

Keeping customers from cancelling their subscription is absolutely essential to growing a stable, reliable revenue stream and growing your business on the whole.

Customers cancel their subscription for all sorts of reasons. They might not understand how the product works.

Maybe they didn’t find that the product was useful enough but misunderstood some core functionality. Maybe they just don’t have the money but would be willing to stick around if a discount was offered.

By automating the process of asking questions to those that cancel their subscriptions, you can reduce your churn rate in a hands-off way.

Even something as simple as offering a discount to those that cancel will lower your churn rate and improve your revenue over time.

It’s an excellent task for automation because handling this by-hand would be very time-consuming.

Example #6: Social Media Automation & Scheduling

Social media automation is one of the most common examples of marketing automation.

It was one of the first marketing automation strategies to make its way into mainstream digital marketing.

While you will always want a real person behind some of your conversations on social media, a large percentage of your social media tasks can be automated.

Things like sharing content, thanking new followers, or sharing posts from partner organizations can all be easily automated and grow your following over time.

Example #7: Referral Marketing & B2B Customer Retention

For many companies, referral marketing is their best source of new leads.

Nothing beats having a product recommended to you by a trusted friend: 92 percent of consumers trust recommendations from people that they know.

You can use marketing automation to automate many of the mundane tasks required in running a referral marketing operation.

You could offer discounts on future purchases to customers that refer your product to a friend and track their referrals through automation.

You can also promote your referral program through regular broadcast emails that make new customers aware of the program and the benefits that come with it.

Marketing Automation is the Future

Marketing automation will play a huge role in the future of digital marketing for large and small businesses alike.

For now, while so few companies have fully embraced and adopted automation, it provides a clear-cut way for small businesses to compete with larger brands.

Using marketing automation consulting to help you install marketing automation solutions into your business practices could very well be the best decision that you make for your digital marketing efforts.

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10 Common Performance Testing Mistakes and How to Avoid Them

Here are some mistakes you should avoid.

1. Failing to test for performance
2. Having no methodology
3. Neglecting to define KPIs
4. Failing to choose a tool
5. Testing for performance at the end of the development cycle
6. Testing only over the LAN
7. Looking only for server crashes
8. Analyzing results at the end of long tests
9. Analyzing each test by itself
10. Ignoring production data

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понедельник, 21 июля 2025 г.