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  1. CDP Newsletter :: The ° Customer View – Dispel the Myth!
  3. Customer Reviews

In this book, technical The Seven Myths of Customer Management is required reading for everyone who's job interacts with or impacts customers. Written in a lively, readable and anecdotal style, this book challenges much conventional wisdom and provides a powerful antidote to many potentially dangerous and expensive misconceptions In an age where companies and financial institutions are keenly focusing on managing the financial risk of their operations, the implementation of quantitative methods and models has been of tremendous help, allowing firms to analyze and manage their risk more efficiently and effectively.

However, the This book helps managers move beyond the idea that the future of business will resemble the past and allows them to use scenarios to imagine multiple perspectives. The concepts of organizational realities, experience, and beliefs are explored to encourage and embrace change in business organizations In The Solution Path, Tasos Sioukas combines practical techniques and tools with spirituality, life skills, and an emphasis on relationships and teams. He presents proven methods that enable readers to take action and create solutions. Unlike other books on the subject that leave readers thirsty This volume makes investing in the stock market a reality by walking investors through the actual process of trading.

George Fontanills begins by familiarizing readers with the field of investment - its history, mechanics and rules - then launches them into the specifics of actually using this A manager argued that he could either increase his business unit's margins or its sales, but not both. His chief executive reminded him of the time when people lived in mud huts and faced the stark choice between light and heat: punch a hole This is a realistic guide to day trading today's stock market. Based on author Josh DiPietro's ten years of experience as a This definitive guide to valuation is written by a who's who of today's top practitioners.

Warren Buffet has said that investors need only look at his operating companies to understand his investment approach. This book provides them with that exclusive look. Berkshire Hathaway insider Robert Miles draws upon his own experiences and those of Berkshire executives, to provide a look This book is designed to help readers grasp the conceptual underpinnings of time series modeling in order to gain a deeper understanding of the ever-changing dynamics of the financial world. It covers theory and application equally for readers from both financial and mathematical backgrounds.

With the current changes driven by the expansion of the World Wide Web, this book uses a different approach from other books on the market: it applies ontologies to electronically available information to improve the quality of knowledge management in large and distributed organizations. This book will show you how to find trade and investment opportunities in the financial markets. The very nature of trading and investing makes it a difficult business. The uncontrollable human emotions that rotate around greed, fear, and hope, are the elements of the human Chapter 1.

Practical Aspects of Transfer Pricing. Chapter 2. Know how they make purchasing decisions that impact your revenues, then influence those decisions through ABM. ID resolution. A CDP can help by organizing collected data points, making them actionable throughout the funnel. You can better know your customers through their unique identifiers, which helps you engage them.

  1. Functional Analysis: Vol.II!
  2. Six Myths of Product Development?
  3. The ancient secret of the Flower of Life. Volume 2 : an edited transcript of the Flower of Life Workshop presented live to Mother Earth from 1985 to 1994.
  4. The Customer Success Book?

It does this by resolving identities and enriching relevant information through the use of cookies, device IDs, or IP addresses. With now-relevant customer data in hand, you can more engage with known customers, not strangers. GDPR compliance. A CDP lets you manage and maintain customer permissions, so you retain them. In order to do so, we need to understand their preferences and their willingness to buy, to learn best what makes them tick.

Some customers always look for bargains and sales, while some get most excited about new arrivals. In order for us to identify these preferences and tendencies, we need to have a broad set of data about each customer, such as their demographic characteristics and purchase history. This data must be integrated from multiple systems, cleansed and matched, in order to allow for segmentation models, predictions and recommendation engines to produce reliable results. Most data used for profile creation, segmentation and predictive modeling is historical data, and therefore these processes are non-real time by nature.

In this piece, we will clarify how to use these two sets of data. We know that a transaction could be declined, that the customer may return the item or even submit a negative review. Because we do have these scenarios happening, and more often than not, we cannot rely solely on engagement data obtained in real time. All this does not happen in real time. Once we have the right data, we can create multiple customer segments and apply predictive models that will help us tailor the right message to the right audience. In many cases a single customer could be eligible for multiple campaigns, and if these are all sent out on the fly, without considering what other campaigns were already sent or what other campaigns are going to be sent later that day, the same customer could get contradicting offers or a less-relevant campaign that could result in a missed opportunity.

We should always plan our marketing campaigns in advance and set a framework of priorities and exclusion rules, so the system can orchestrate all these scheduled campaigns and optimize the communication to each customer.

Once we manage to get our customer into the store, we want to switch to a different mode of operation: We still have all our pre-processed customer profile, but now we also have live activity feed from the store that we need to use in order to respond in real time to specific signals coming from the customer. For example, if we know that a certain customer is a deal seeker and we see her currently looking at non-discounted products, we can point her to the clearance section of the store.

So far, we talked about how in-store engagement is a real-time process, while profile creation and smart orchestration are not real time. But there is also a third option in between, in which the marketing plan is built in advance but adapts itself to short-term updates. Consider the following scenario: We plan a campaign for some of our one-timer customers, trying to encourage them to make a second purchase, and we schedule this campaign to be sent this afternoon.

But right before execution, we find out that a couple of these customers just returned their last purchased items — which significantly changes their profile and the way we want to approach them. For them, we are no longer trying to activate one-timers but rather save them from churning. In this case, and even though we already established the optimized list of the one-timer campaign recipients, we need to re-evaluate the audience just before the actual execution time in order to make sure we exclude any customer that no longer matches the desired profile.

This example shows how a combination of fast and slow data processing and modeling can help reach the optimal result. But to get the full picture and cater to the ultimate goal of reaching the right customers with the right offers at the best time requires not only to know the difference between slow and fast data, but more importantly, that they always go hand in hand. Research from National Express suggests that, on average, Britons book their next holiday just 37 days after returning home from their previous one.

The ability of a business to retain its customers is crucial for its growth and success. In order to do so, many include in their business objective decreasing the percentage of single purchasers, while increasing the number of multiple purchasers and therefore the overall customer lifetime value CLTV.

Little nudges that show how much you care can encourage your customers to come back, again and again. For a first to second campaign to be profitable, the best you can do is to maximise your resources, distinguishing between the customers that are worth pursuing to those that are not.

The insights from customer data are your best chance to get to know your customers on a deep level and, therefore, to create communications that will engage and interest them. Data can help you build a profile of the customers most likely to make a second purchase, analysing details such as how much time passes between the first and the second purchase, or how much discounts and peer reviews influence them. Of course, all of this is the more efficient the less your silos are separated, but more on that in a future blog post.

Say you have the optimal database at your disposal, one that can deliver you a single customer view on all channels for each one of your customers. You have now a complete understanding of your customers. The final stage is to move to a predictive model , whereby customers are assigned a likelihood to become first to second purchasers, allowing you to target those with high and low likelihood with more advanced tactics.

You can use these insights to create marketing automation strategies which will further maximise your resources. One way of increasing customer loyalty, for instance, is to make sure you maximise the excitement of their first purchase with you. Ultimately, the devil is in the detail. For some tangible advice, take a look at this infographic our in-house strategy team have put together, with tips on converting your new customers into loyal multi-purchasers.

Originally posted on the RedEye blog. For almost two decades, tech experts claimed the future would be mobile. With an estimated 2. Advertisers and marketers have clearly adapted to this new reality, seeking out new ways to turn traditional marketing strategies into effective mobile campaigns. Even major brands like Amazon have introduced customizable ad services that are gaining momentum in online and US retail spaces.

In , mobile marketing has turned into a multi-channel discipline that supports massive segments of our online ecosystem. Yet, this future arrived so quickly that it can be challenging to explain or even grasp many of the seismic shifts this field experiences on an annual basis.

Every month, our experts will sink their teeth into another aspect of this fascinating field, with hope to inspire you to elevate your business through some smart marketing. Check out our features section with special projects and articles for your reading pleasure. As the term implies, mobile marketing is a technique where advertisers deliver communications to users via smartphones and tablets.

As simple as that description sounds, mobile marketing encompasses a broad range of delivery channels including email, SMS messaging, push notifications, in-app advertising, QR codes, and many more. Advertisers can deliver personalized messaging, deploy ads based on time of day or location, and design interactive ad formats that effectively engage specific demographics.

Customers use mobile devices to play games, watch movies, and communicate via social media — all fertile ground for marketing opportunities. The significance of mobile devices is even higher in emerging economies, where cell phones have become the easiest method of gaining internet access. Meanwhile, in the developed world, the volume of online content accessed using smartphones has eclipsed traditional platforms such as desktop computers.

In-app mobile marketing, sometimes referred to as app-based marketing, refers to the deployment of advertisements directly within an app itself. In order to monetize their apps, developers often integrate ad network SDKs that display ads when certain conditions are met.

Some app publishers like Facebook even use Promoted Post services that seamlessly integrate ads into news feeds across all devices. One important variant of in-app mobile marketing is in-game marketing, where advertisements are deployed directly within a mobile game. While there are certain ad formats and deployment considerations when delivering messaging to gaming audiences, marketing SDKs function in fairly similar ways to in-app mobile marketing on a technical level.

SMS mobile marketing is the earliest form of the technique, first implemented when SMS and shortcodes launched in the early s. It requires advertisers to obtain or capture mobile phone numbers and directly communicate with users via SMS messaging services. SMS mobile marketing can refer to both inbound marketing strategies for lead generation and outbound strategies to communicate promotions and events.

While SMS mobile marketing has been overshadowed by in-app advertising, it still remains a powerful strategy. That makes it an impressively effective strategy for rapid engagement with a large volume of potential customers. More importantly, SMS mobile marketing is widely used internationally, especially in regions like Europe and Southeast Asia.

This broad reach is largely thanks to compatibility with non-smartphone cellular devices.


CDP Newsletter :: The ° Customer View – Dispel the Myth!

SMS marketing is more strictly regulated than other marketing channels, but tends to benefit from having clearly defined best practices that are standardized through cellular carriers. These notifications serve a variety of purposes, most commonly to inform users of incoming messages from social media apps.

From a marketing perspective, push notifications are an ideal format for keeping users in the loop about new promotions or app features. Above all else, the primary driver behind push notifications is customer retention. Studies consistently show that push notifications can increase day user retention from 3x to 10x depending on the effectiveness of your messaging. QR codes are a type of matrix barcode that can be scanned by a mobile camera, usually activating a web link in the process. In mobile marketing, this allows advertisers to combine physical and digital marketing techniques by displaying QR codes in the real world.

In the hands of mobile marketers, QR codes are unique tools that appeal to human curiosity can be placed anywhere, and are easy to track. Unfortunately, QR codes are also not as intuitive as other marketing strategies on this list, and tend to be used by a smaller subset of mobile users. That said, QR codes can be useful when deployed effectively, and are especially popular in regions like China. Mobile search ads are standard search engine advertisements that are indexed and optimized for mobile devices.

Google search ads can also feature a click-to-call button or click-to-install button as a call to action for your customers. Always keep your audience in mind. Be concise. Smartphones have limited screen space for deploying your message, and there are literally thousands of things users could do instead of viewing your ad. Get straight to the point, and give them a reason to engage with you.

Optimize websites for mobile devices. Much like our last point, transferring desktop-optimized web pages to mobile devices usually means your marketing efforts are lost to clutter and noise. Design mobile-specific versions of your sites that are optimized for on-the-go smartphone users, and build your marketing campaign around them.

Adopt multiple marketing strategies. There are many mobile marketing strategies available to advertisers in Benchmark your results. Keep track of how users interact with each of your mobile marketing strategies. Follow conversion, retention, and engagement metrics to maximize your ROI. Mobile marketing is a far more complicated field today than it was in , but there are also far more ways to engage with your audience than ever before.

By adopting the strategies listed above, your business will be well underway to expanding your reach across a variety of active channels. At a recent event I presented six case studies about the successful i. All the case studies were based on the application of Predictive Analytics to help target customers or prospects at various points along the customer journey, for instance, identifying the single buyers most likely to make a second purchase or which VIP customers were at greatest risk of lapsing.

But I had fallen into the trap of failing to explain what I meant by each of the definitions I was using. So, very briefly:. Behavioural data has always been critical. It is the core of data-driven personalisation.

Product details

Using this information to work in combination with engagement and transactional data identifies prospects and customers in terms of what someone will do next and when. Gartner are now stating in their Hype Cycle that Predictive Analytics has fallen into the trough of disillusionment! And with so many marketing tech businesses out there are talking about it, but not many are able to demonstrate the real value it can bring for retailers. This really shows the disparity between the desire to implement Predictive Analytics vs. The aim of the presentation was to try to reinforce the potential value of these tools to the market, but the key is to start with data.

Customers are getting more and more difficult to understand with the proliferation of marketing channels. Just recently WhatsApp was added into the fold, yet another channel that marketers can target their consumers through. This is where AI comes in! Start by understanding your data. Where is it coming from? He was right: putting in the leg work at the beginning and creating a true Single Customer View was key.

A Single Customer View means you can tie together transactional, engagement and behavioural data, allowing you to paint the full picture of your customers. Finally, it is key to apply AI and Predictive Analytics to something tangible. At RedEye, our predictive models are based on the customer lifecycle.

By making incremental improvements at each of the key customer moments, you can see substantial increases in overall customer value. The CX concept was introduced in by Holbrook and Hirschman as a holistic construct. In the meantime, both academics and practitioners believe that a favorable customer experience not only positively impacts customer satisfaction, customer loyalty, and word-of-mouth behavior — something customers themselves have known all along — but that it also is a compelling precursor of the much-coveted competitive advantage.

Despite this consensus, the CX concept remains foggy because the holistic construct has diverged into two mostly unconnected schools of thought. The main reason is that academics and practitioners tend to look at customer experience through one of two opposing lenses. One is the organizational lens and the other is the customer lens. The one who looks through the organizational lens assumes that experiences can be designed and that all customers will perceive stimuli alike.

The one viewing through the customer lens ascertains that firms cannot deliver value since the customer is always a co-creator of value. While the former focuses on organizational structure, strategy, and customer-employee interactions, the latter considers individual customer journeys, cognition, affect, and senses. A distinction is made between static and dynamic customer experiences. A static CX describes how an individual evaluates one or more touchpoints with an organization on a cognitive, affective, and sensory level at one specific point in time.

A dynamic CX considers the evolving cognitive, affective, and sensory evaluation throughout the entire customer journey. The organizational lens points to the design of static CX and to the management of dynamic CX, yet the focus on static CX tends to dominate. As one can see, the famous customer journey is a key component of the customer lens. Being taught requires a willingness to learn and to shift to an outside-in view.

Yet most organizations fail to truly master the art of customer journey mapping. Three reasons account for this fact. First, looking through the wrong lens sets one up for failure. Secondly, the proliferation of touchpoints, channels, and offerings makes a customer journey non-linear, unclear, and unpredictable.

Thirdly, in this day and age of big data, data still is unable to capture the emotions and feelings of customers. Among practitioners, the growing consensus is that one must look at the dynamic CX and not just at the static. Moreover, it takes the entire organization to support the customer journey. Two precursors to CX success are:. The first is the select group of people who unlock most value for your organization. Yet only hyper-relevance engages both customers and employees. Brown, L. CMO Council, Milbrath, S.

The fundamental flaw in customer journey mapping—and how to fix it. In fact, according to Walker , customer experience will supplant product and price as the key differentiator by Coordinating all touches along the journey is known as delivering a unified customer experience. And it starts with unified data, which helps explain why solutions that help unify data are top of mind. The best place to start when assessing technology is always with a defined use case.

Yet these are essential for analyzing disparate customer and prospect data and combining them into a unified view. Moreover, they lack out-of-the-box integrations with go-to-market systems and digital channels. Take the integrations for Snowflake, a leading cloud Data Lake. They do not integrate with the channels and solutions a marketer would need to run an effective campaign. Since data is dumped into the lake without any up-front restructuring, resources are needed to apply advanced technologies to explore and gain insights from the data. This forces revenue teams to request data and reports from the IT department.

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  • Because these requests are not an IT priority, they often linger for weeks and sometimes months. Go-to-market teams then have to prep the data for activation in its downstream systems — enriching and restructuring the data, such as validating email addresses and phone numbers — so it can be used effectively by sales and marketing. If the purpose of unifying data is to enable real-time decisions that help orchestrate a unified customer experience, then Data Lakes fall short. They make it possible for organizations to ingest and link customer and prospect data and all its detail from virtually any source — including third-party sources — in real time.

    Plus, CDPs make that record easily accessible on demand so marketers, sales, and revenue ops can ensure personalized and highly relevant customer interactions at every touchpoint. As a system that creates a persistent, unified customer and prospect database accessible to other systems as defined by the CDP Institute , a CDP better serves revenue teams. In fact, go-to-market teams can easily share and activate data — and change or add system sources — without disrupting the CDP.

    And, in many cases, enterprises already have — or need — both. But the similarities end there. IT-managed Data Lakes ingest enterprise-wide data — typically first-party data from internal sources — without altering the data form. On the other hand, marketer-managed CDPs unite first- and third-party data and enable a real-time flow of data into and out of the system.

    With easy integrations to all channels along with built-in tools for even business users, a CDP makes it easy to view, pull, and analyze data and arrive at audience insights. Because of this — and the addition of third-party data — CDPs help enterprises improve their targeting and customer experiences.

    Moreover, easy channel integrations that yield net-new data pave the way for faster time to market and expanded customer reach. We could use this information to better look after our customers and sell better products and services, tailored to their needs. Clearly an army doctor, then. He has just come from the tropics, for his face is dark, and this is not the natural tint of his skin, for his wrists are fair. He has undergone hardship and sickness, as his haggard face says clearly. His left arm has been injured. He holds it in a stiff and unnatural manner. Where in the tropics could an English army doctor have seen much hardship and got his arm wounded?

    Clearly in Afghanistan. Well, maybe in those simpler Victorian days. But in our modern world, Sherlock might not be able to make sense of all of the data and may very well get it wrong:. Here is a gentleman of a medical type, but with the air of a military man. His creator, Arthur Conan Doyle, knew a thing or two about information, as he shows in his books. He knew the vital importance of possessing all the information, not aggregating or cutting out any data and being able to understand the patterns and connections. Otherwise your energy and attention must be dissipated instead of being concentrated.

    Moving from the nineteenth to the twenty-first century, we have radically changed the way we gather, analyse and interpret data. Customer journeys are mapped out at great expense to describe the customer experience for 10 to 20 different customer types. Telcos do this because the data they have about their customers is limited. And most of the time it is also aggregated. They have CRM data, loyalty scheme data, third-party socio-demographic data, customer care contact data.


    This data tells the telco teams how much a customer has spent, what products they have, what they last complained about, or their propensity to churn. Laughably, some telcos call this their degree customer profile. This level of data used to be okay. But the world has changed. Humans interacting with humans. A Sherlock who gets his deductions right would look like a man of authority and intelligence — someone you could trust. Telcos face the same problem. They can deeply understand their customers, but they can and have alienated them by making broad assumptions based on data aggregation rather than personalised knowledge.

    Like a poor detective who keeps deducing wrong outcomes based on misinterpreted information. Customers lose confidence in the telcos, become frustrated and even start to mistrust them. How dangerous it always is to reason from insufficient data. With so much to gain from a CDP deployment, it makes good business sense to spearhead the initiative within your organization. But how do you convince senior management to take that next step and implement a CDP? We know that presenting a new budget item to senior management can be a hard sell, especially when they may not be familiar with CDPs, so we created a plan to help.

    In our guide, Building the Case for a CDP in Your Organization , we lay out four steps to help you demonstrate the marketing, operational and financial benefits of a CDP to your senior management. The first step involves demonstrating how a CDP will improve the bottom line by creating greater efficiencies in personnel and resources, and allowing for more automated, higher velocity marketing. Next, make the case by showing how, even though a CDP will be owned and managed by marketing, it can positively impact other departments in your organization.

    Departments like IT, Finance, Merchandising, and even the C-Suite itself will realize improvements in effectiveness and reductions in cost. Further make the case by introducing real-life case studies which show the positive results realized when retailers implement a CDP in their organization. Our guide provides examples of two such cases. Finally, address the financials by demonstrating that many retailers see a return on investment in a CDP in under a year and some even see increased ROI in less than six months, depending on organizational goals. Telcos need to up their game if they are to keep pace with the expectations of their customers.

    The key to this is the development of a more meaningful and relevant personalised experience. It has transformed from a clunky curiosity to a powerful pocket computer that is all things to all people. During that time, we have grown accustomed to the benefits of personalised products, services and messages. We receive traffic updates during our daily commute, listen to automatically generated playlists and travel with an umbrella because Alexa warned that rain was likely.

    No matter where we are, we expect our technology to cater to our individual needs. The fine line between convenient features and personalisation has not always been this clear. While these features were milestones of the mobile generation, customers still did not expect their mobile phone provider to consider them as an individual. They never see customers merely as users of their technology. This required them not just to know whether John had trouble finding the right product online or the next series to watch, but rather knowing that John has two sons, the name of his favourite football team and the fact that he believes ski vacations are just too expensive for the whole family.

    To achieve this, they need data and the ability to predict choices, spending patterns and future behaviour. As their marketing reach grew more powerful, we came to rely on things like Facebook birthday reminders, targeted shopping advice from Amazon and the fact that Google really does know where we live. Since the introduction of GDPR and international laws to follow, data sensitivity and a transparent correlation between data provided and returned value is fundamental to successful customer relations.

    Judging by these building blocks, telcos have the same, if not greater opportunities to create personal customer experiences. Successful companies have created truly loyal followers, not just through the superiority of their products and services, but by building highly personal relationships with their customers right from their first interactions.

    Companies such as Apple, Nike and Instagram have created loyal followers, guarding the business from bad publicity and supportive of the idea that not every new product release will be superior to the competition. Marketing and Customer Service lead a crucial role in establishing not just professional, but highly personal relationships with their customers.

    The Importance Of Customer Relationships

    That said, customers are - of course - fully aware of the fact that a company with millions of users is not able to create a one-on-one personal connection. They do, however, expect an engine of intelligence smart enough to respect the customer as an individual with complex needs and unique circumstances. If automated intelligence in marketing and support is paired with an informed customer service agent, customer interaction can create highly personalised experiences. The customer journey can be narrowed down to a few key experiences.

    We can all recall situations of calling customer service because we cancelled a service too late, we thought we were billed incorrectly or we finally picked up the phone after receiving 13 unrelated marketing communications. What customers remember is not necessarily the problem, but how it was handled by the business they engaged with. Lastly, in times of digitally shared knowledge, negative as well as positive experiences with customer service can be spread rapidly and at large, potentially risking your relationship with the rest of your customer base.

    Telcos are facing an opportunity impossible to ignore - the market is demanding personalisation and most telecommunication providers are using little to no technology to address this need. We consider three ways in which telcos can transform their operations and improve their performance. Tackling these issues will enable telcos to compete in the market going forward.

    The challenge is significant and impossible to ignore. All marketers are storytellers. As a marketer, you identify key audiences, get to know them, then craft a narrative that helps each of those audiences picture themselves using your product or service and being happier as a result.

    It may seem like the marketers on the front lines of Hollywood and the publishing industry have it easy — after all, how many potential ways are there to sell people on a new superhero movie? And with digital marketing ushering in a new era of targeting capabilities, marketers are able to take a much more surgical approach to reaching the people who will ultimately be interested in their content.

    The new challenge is getting those audience profiles as accurate and detailed as possible. And this is how entertainment marketers want to approach their jobs across the board. Because unlike Netflix, marketers do not have built-in algorithms. The answer is probably both, but if a marketer only identifies with one of those groups, they may ignore or downplay the other category.

    And therefore, they may totally miss out on some valuable audience profiling data. You may see where this is going already. To help entertainment marketers be more effective, we can equip them with the data and algorithms they need to make informed, surgical marketing decisions. My company StoryFit has a database of millions of film scripts, teleplays, and novels, with new ones being added every day. Our AI breaks down those scripts to make them easier to categorize across detailed metrics like the personality profiles of the characters, thematic elements, the type of story arc rags to riches, etc.

    Marketers can find comps to their current project, see exactly where the similarities exist, and use that information to target audiences who responded well to the comparable content. They can also look back at marketing campaigns for those properties and see, for example, how they were structured tonally and what aspects of the story they focused on to make more informed decisions about their own campaigns. Comps can even be helpful when determining — and defending — marketing budgets. Producers who find themselves reverse engineering scripts from scratch based on what has worked in the past are likely to find the results disappointing.

    The human brain is well-tuned to identify unoriginality , and playing it too safe will come back to bite studios and publishers over time. Are my characters registering as complex, and are they sufficiently different from each other? Do they have characteristics that are usually reserved for male characters are they trailblazers? Which famous character is my leading male most similar to, and am I happy with that comparison, or should I rethink a few things to make him fresher? AI is one valuable voice in the room. Machine learning should help marketers across industries become superheroes, not zombies.

    So as you start to craft your marketing stories, look to AI where possible for data that makes you smarter. Then let your humanity take the wheel, with clearer knowledge of the best routes and the lay of the landscape ahead. Telco Customer Experience and Marketing teams are failing to make emotional connections with their customers. They are frustrated because they are unable to use all of their data to develop rich customer insights.

    To add to their frustration they see other companies using data in a more personalised and engaging way than they are - we call this the Great Competitive Paradox. Local shopkeepers know their customers personally because they meet them and speak to them regularly. They build relationships based on familiarity and trust, and that makes doing business easier. He lives in New York and has a low propensity to churn.

    Add a little more data and you discover he also has another contract for an iPad and a prepaid account. What about other product lines? Third-party data tells us the household details and that John is married to Jane. So now we know John is married and the household view shows associated fixed and mobile services. This additional data indicates they have a child. So now we know his family and domestic arrangements, the phone he uses, how much he spends, and we have a somewhat blurry picture of his relationship with his service provider.

    We can learn more. What about service usage? Perhaps John experiences higher than average dropped calls or maybe has a poor data experience. We should include this quality of service information because that will provide a view of his experience of the network. We have some good propensity models too - such as propensity to churn or Next Best Offer models. We know which offers he clicked on and accepted or declined.

    This level of customer insight is usually the best it gets for a typical telco. In the past, this level of understanding was ok, but the world has changed. We need to add a lot more insight in order to gain a deeply human understanding of John and in turn give John a highly personalised customer experience. We have the details of who John calls and messages and who calls or messages him in our billing records. We can use this information to identify key interests. We know what websites and apps he visits, so we can use that, too.

    Data is fine, but understanding is the key to greater wallet share. You have to add meaning to the data; otherwise, it is useless. The fact that John frequently visits a website tells us little. Is it a coffee shop or a clothing retailer? If he calls a number, is it a ticket line for the cinema or is he calling a friend?

    The key to all this is to process the data in a way that allows you to create a micro-database for each and every customer. You can add meaning to the data which in turn delivers a depth of insight never experienced before. Instead of knowing John frequently visits the usual suspects in terms of websites, you now know he has interests and brand preferences.

    He likes coffee, football, dogs and Formula 1 racing. Once you understand the behaviour, you can also infer attributes. Remember John has a prepaid account? The interests associated with this account are tennis, cosmetics and university. Targeting a football-related loyalty offer to this customer is not going to be successful. The behaviour suggests an year-old female, probably their daughter, and John pays for her account. You can micro-segment like never before, create tribes of customers with similar behaviour and target with enormous precision to deliver a personalised experience. This means no more spam.

    Now you are processing your data and giving it meaning. The profile is as complete as your data allows. See how O2 have started to challenge the Great Competitive paradox link here. Contact me at mark. Ever come into your office, look at your calendar, and see right there in printed black and white that today is the day of your big presentation? You walk into the meeting with the higher-ups, sharing how the marketing campaigns are doing great, feeling confident about the progress.

    I know what I used to do. By this point, much of the morning — not to mention lunchtime — would pass by with little progress. I knew a change was due. The first step? Standardized Autonomous Reports. The type you can find inside your ESP or marketing automation platform. A report provides the exact number of customers who performed a given action.

    If the system is really advanced, you would even have a dollar amount attached to those numbers. Standardized reports helped me identify which campaigns had better CTRs and which generated a strong uplift in revenue. They took much of the manual work out of my day-to-day. But I felt we could take this further.

    There are many articles out there on why marketers need next-best-action recommendations or insights into missed opportunities and potential optimizations. None of which is apparent from standard reports. Additionally, with a small team, it eventually became too difficult to measure our entire operation. The second step of my journey led me to Smart Reports that could help marketers detect customers at risk of churn and which campaigns could bring them back from near abyss. Ones that provide insights into which campaigns had a significant impact on customer lifetime value or lacked any sort of impact at all.

    And to better answer those company-specific questions that await at the management meeting, we have step three: a Consultant or External Supplier who could help create custom reports with the insights I needed: impact of my marketing efforts for my VIPs, aggregated campaign reports filtered by customer lifecycle and the like. I was now an independent data-driven marketer, armed with the tools to prove how campaigns impact the business in any metric we could come up with. I felt too dependent on this external team to send me the report at a certain cadence.

    Like most marketers, I want access to customized reports filled with the most recent data whenever I want. Enter integrated BI Dashboards. Customized and dynamic, integrated within the marketing solution of my choice, and always up to date. This solution allowed me to slice and dice the data in a visual and intuitive manner. This was the final step for me, the stamp of approval for my personal growth as a modern marketer. This journey did not take a day or a week. It began with understanding if the team were to achieve marketing agility, then we needed to own the data.

    From customer lists to sales results, we required access under one interface. We had to truly understand what the most important metrics were. We had to acknowledge the limitations, know when help is needed to uncover the deepest insights from marketing efforts either through smart reports or an external consultant. And finally, dream big. Think hard about the most dynamic report that could answer the toughest questions in an interactive way and find the BI tool that could deliver this promise.

    This week we saw interesting and exciting announcements from Adobe and Salesforce. We will discuss our detailed thoughts on these product announcements in future blog posts once they share more details. But, to start with, we want to extend a hearty welcome to Adobe and Salesforce to the Enterprise CDP party -- a party AgilOne started and been at the forefront of for several years already.

    Both announcements acknowledge the definition of a CDP to include, at a minimum, the following features:. In fact, we at AgilOne have been saying this all along. We worked with analysts the CDP Institute, Gartner, and others and the market to arrive at this definition of a CDP based on our exciting and growing enterprise customer base. Contrast that with how less than a year ago, these and other vendors dismissed CDP as a passing fad , while at the same time they made attempts at partial solutions that were not well received by customers or analysts.

    The market forces were too strong for them to continue this approach. Because of this, we have a unique perspective:. All these new entrants to the CDP party will need to go through the same growth curve as they move from this announcement stage to actual customer usage. So, Salesforce and Adobe, we welcome you to the enterprise CDP party and to a growing and exciting market! We will be back with our thoughts on their product announcements in another blog post though we are not holding our breath for those vendors to develop and publish more details any time soon Until then, here are some interesting articles for your reading enjoyment:.

    For those of us who have been building this category for the last 5 years — we want to say welcome to the club! This is a huge acknowledgment from the two biggest players in marketing technology space, so we wanted to provide some context and clarity around the announcements and how we view this shift. Their pedigree is in content think Adobe Photoshop and business applications think Sales Cloud. Fundamentally, these are execution-first products with very limited data capabilities. This is BIG. Not just for the marketing clouds, but also for the Customer Data Platform market.

    Fast forward to just a year later, Salesforce is finally committing to check-the-box CDP, and Adobe announces a another? What changed? Ultimately, the market and their own clients forced them to make this change. We know for a fact that in the last months both Adobe and Salesforce have lost out to smaller startups with CDP capabilities that power personalization at scale. We also know that once the honeymoon wears off from an Adobe or Salesforce marketing cloud purchase think months , enterprises start to realize that data integration and unification is not their core competency, nor the reality of their offering.

    Both the market and clients have succeeded in pressuring the marketing clouds to acknowledge their existing data gap and promise a future solution. Now for the most important question, how should brands evaluate these announcements against their marketing strategy and capabilities? This can be summarized in 3 simple questions all brands should ask themselves:. Building a CDP requires deep expertise in data integration and architecture which becomes the foundation of the entire platform. Building foundational data infrastructure takes time. And not the good momentum, but the kind that makes doing anything new extremely difficult.

    Change is hard, and when it comes to something as foundational as core data infrastructure, it takes a long time. If you can afford to wait, does it make sense for you to go all-in on a single cloud? To answer this question, you have to ask another question — do you expect Adobe or Salesforce to be the only vendor you ever use, i. Even if you believe Adobe or Salesforce can build their own CDP in a reasonable amount of time, you would still be constrained to only the channels that live within their cloud.

    At ActionIQ, we believe that brands should have the flexibility to choose best-in-class channel solutions for themselves and reduce switching costs when their strategy changes or the vendor falls behind. In closing, we see the last week as a massive win for the Customer Data Platform market and, more importantly, for brands wanting to be more personalized and customer-centric. CDPs will be a foundational layer in the enterprise marketing stack going forward — powering personalization at scale while optimizing marketing performance and efficiency.

    With these announcements, everyone has now acknowledged that single view of the customer is an unsolved problem and we can all move on to evaluating who has the best solution to solve it not just in the future, but right now. By defining their vision, Salesforce does in many ways help clarify where CDPs fit in the tech stack and how they could be used to ultimately create compelling customer experiences for consumers.

    Customer Data Platforms may be the technology category with the most variance across vendors, with one product looking a lot like BI, one like marketing automation, and the next bordering on master data management MDM. So I welcome any large cloud provider like Oracle CX Unity or Salesforce raising awareness of the category and guiding the market toward a standard definition.

    I see several gaps, or details that were left out or intentionally overlooked, that will mislead the market toward the CDP that Salesforce wants you to see. It is scalability, flexibility, and trust. First, scalability is critical to a customer data platform. Customer data growth is exponential across volume, variety, velocity, and veracity. The platform must be able to handle massive loads of customer data at once and respond as the data changes.

    Data storage, matching and validation pipelines, and APIs all must be built for scale. Imagine the data from several enterprise CRMs, marketing automation platforms, dozens of third-party data providers, and data lakes all synced and managed by a single platform. Lastly is trust built from a focus on security, privacy, and responsibility. Enterprises across every industry often claim they are becoming technology and data companies.

    Customer data has become their most precious asset. Platforms must have security and privacy as a pillar to both their technology and ethos to work with enterprises. They also must showcase the values enterprises so desperately as trying to encompass. Is positive impact part of the pitch? Are you using data for good versus evil? How are you giving back? No knock on Salesforce here. They have enterprise solutions and have mastered selling them.

    I look forward to seeing if their CDP lives up to the criteria of enterprise-grade. Breaking down a Customer Data Platform into its various parts and where it sits across your technology suite is a worthy exercise. These actions can be very tactical, like enriching CRM data, or very strategic, like migrating from on-premise data lakes to cloud data management. Next is differentiating with rich customer experience.

    How do we use the master customer data to create faster, more personalized, delightful experiences for our customers? Building platforms on top of a shaky foundation puts big promises for customer experience at risk. End-to-end Customer Data Platforms must fully commit to their role in data transformation. CDPs that address fundamental data accuracy and unification open up a smattering of other use cases under the umbrella we call Revenue Ops.

    Customer Reviews

    Data is essential to the revenue-focused responsibilities of operations experts who:. A sales ops team with a CDP that offers a robust data management toolset can do better routing, territory planning, forecasting, and strategic planning. Data teams can use CDPs to find data redundancies across internal and external sources that lead to significant cost savings. Strategic groups concerned with data portability can use the open, scalable data infrastructure of a CDP to break down enterprise-wide silos for a broad set of needs. Is Salesforce definition of a CDP too narrow? They could make the argument that all these technical solutions are in pursuit of better customer experience.

    We believe the applications and gains for revenue ops are so significant that they deserve their own call out. As usual, solutions built for B2B are always an afterthought. While some technologies can serve both those targeting consumers and businesses, Customer Data Platforms do not translate well. An email to a consumer is not all that different than an email to a business buyer. A consumer website visitor who converts in a shopping cart is tracked similarly to a business buyer that converts on a demo request landing page.

    Data architecture in B2B is built to ingest, match, resolve, and manage a variety of data types: business records, hierarchies, buying centers and locations, contacts, departments, intent data, behavioral data, and countless others. To create a high-performing platform tuned to business data requires years of dedicated work from expert data scientists and engineers. This data is used for enrichment, total address market analysis, segmentation, audience activation, and prospecting lists.

    However, it lacks detail and does not encapsulate the broad applications and technologies that have become known as Customer Data Platforms. This statement from Gartner has proven all too correct for many business struggling to create their own customized B2B data collection and distribution systems from scratch.

    Creating systems of record that integrate data flawlessly and connect readily to outside applications is no small feat. This is especially true in the B2B space. Any organization hoping to build a B2B data management system from the ground up must cope with time-consuming, laborious issues:.