Tag Archives: data-driven marketing

Campaigns Don’t End When You Hit Send: The Importance of Feedback Loops

This article is part of our series on customer experience where we focus on topics relating to connecting data, intelligence and experiences. Further reading: Why Inconsistent Messaging is Undermining Customer Experience.

While many marketers are enamored with the advantages of martech, it is worth remembering the activity should always be optimized. After all, marketing technology is only as good as the data feeding it, and customers’ behavior is constantly providing new data.

Feedback loops used to be directly communicated in person, during a one-to-one conversation. But as we’ve moved from an offline social culture to transacting primarily online, we need to find new ways to understand our customers.

It is now necessary to read customers digital body language to reveal purchasing power, location, interests and intent to buy – among thousands of other attributes.

For many marketers, feedback loops are a significant step up in the quest for better performance.

Targeted marketing is a standard practice. Though, ultimately the output is only as good as the data being input. Too often campaigns are verified through metrics that, in an effort to assess overall performance, fail to provide the granular level of data needed to optimize campaigns.

Fortunately, each customer touch point in a campaign can provide a feedback loop, offering different forms of verification and the potential to close data gaps — provided the right metrics are used.

Take an email campaign as an example. It may use good data segmentation based on a well curated CRM to push out the message. Traditional performance metrics would be the open rate or the click-through metric. They indicate that the audience you targeted actually interacted with that campaign. But the campaign doesn’t end after the email is sent.

The feedback loop would identify not only whether an email is opened or not, but who has and has not clicked through. Each outcome provides an opportunity to improve segmentation and optimize the campaign.

This process relies on an ability to tie actions to the individual. However it is not necessarily about identifying individuals. Digital body language is 100 per cent anonymous. Therefore privacy is maintained as global concerns are raising over data use and privacy mount.

Nevertheless it is a feature often lacking in many marketing metrics and ultimately produces disconnected experiences.

Not All Metrics are Equal

Many of the existing marketing metrics provide useful insights but fail to really optimize campaigns. For example, a media agency may offer measurements aggregated by region or messaging. It shows a campaign has reached a certain percentage of a target audience. It is useful information, and not to be disregarded, but at the level of an individual it fails the optimization test.

There is disconnect, because you don't know which of those people you targeted in the campaign actually engaged with it. It is not enough to know what percentage of an audience clicked through. You need to know who has clicked through, who hasn’t, and why they did so.

The solution is to include metrics that can be interrogated at a granular, individual level - digital body language. When that information is available, it produces immediately actionable insights and a finer level of segmentation. Connecting these metrics to the technology stack improves customer verification and eventually customer data insights.

Through proper feedback loops, campaigns are adjusted on the fly with the ability to target individuals who do not initially engage.

We can also reduce advertising spend by not targeting existing customers and increase our add efficiency by targeted those customers most likely to buy.

In ASEAN, the availability of third-party data on targeted customer accounts might still be an issue for advertising. However, over the coming years we’ll be seeing a lot more data become available as large players start to make their data available via third-party marketplaces such as Oracle’s Bluekai. 

Our customers who have used our Oracle Marketing Cloud to run multichannel automated campaigns (using email, push, and SMS) have realized significant increases in open rates and conversions.

While it appears an advanced use case of data and marketing technology, the current pace of change may mean it is soon table stakes.

Want to learn more? 

Download Seamless Marketing: Elevating the Customer Journey to New Heights

Why Inconsistent Messaging is Undermining Customer Experience

This article is part of our series on customer experience where we focus on topics relating to connecting data, intelligence and experiences. Further reading: Silo Busting is Essential to Delivering Personalized Experiences.

Delivering exceptional customer experiences has quickly become table stakes for marketers. Too often, though, these experiences are undermined by inconsistent messaging and opportunities go begging.

Repeated or irrelevant messages breed consumer intolerance and annoyance, which they are not afraid to shout about to the hilltops.

Inconsistent messaging can also be a lost opportunity. For instance, when a customer expects to be informed, but there is silence. Such as when a customer signs up to a new program and reasonably expects to receive a welcome email. When they receive nothing, that can create confusion and concern — which can be just as damaging as a sending a poor message.

In markets like China, where social and ecommerce platforms are dominated by a few large players, the risk and reward of consistent messaging increases, particularly for B2C companies.

For example, WeChat and Alibaba both have an incredible reach. And, given their prominence, consumers often use both platforms. So, any inconsistent message on WeChat can quickly undermine strong messaging on Alibaba, and vice-versa.

Compounding this problem, marketers sometimes focus too intently on WeChat and Alibaba and neglect their owned channels of email, SMS, and website. The messaging in all channels must be relevant and consistent.

Why It’s Happening

This isn’t rocket science, but it still trips up many marketers. The reason? The ubiquitous problems that arise from disconnected data systems and data access – marketers and systems in silos. Marketers simply do not have a single view of the customer, much less an accurate idea of what messaging has already been delivered.

That problem snowballs when channels are managed by different teams — such as a media agency for acquisition and remarketing, another agency for social marketing, while a company’s own marketing team manages email and mobile channels.

When this happens, even a central marketing plan can’t connect the data and creative for individual customer experiences.

Many organizations still lack the skills and tools necessary to unearth customer insights from first-party data. Those insights are needed to improve customer experience and deliver consistent, relevant messages through all channels automatically.

How to Fix It

A great place to start is to build consistency on the areas over which you have control and where you are comfortable.

For example, implementing automation and template programs for email and mobile channels will improve consistency in message cadence and content. At Oracle, we recommend leveraging existing data and using dynamic content to personalize your messages while maintaining a consistent message.

Next, build a data strategy to inform segmentation and start to weave that in other channels. It’s likely that your first major roadblock will be addressing how customer data is managed and accessed. Therefore, when getting data architecture in order, the focus should be on creating a core customer view in a secure, transparent and privacy-compliant way. All other data — such as sales, product, and policies — can then be attached to the core customer data, creating the fabled 360 degree view of the customer.

It is no small feat to upgrade data architecture and automate marketing. However, the benefits that accrue will quickly justify the undertaking.

Want to learn more? Get the Cross-Channel Orchestration Fundamentals Guide to learn how you can give consumers the personalized, relevant, and consistent experiences they want.  

Putting Dynamic Back into Email Design

While it may surprise those who view emails as an antiquated channel of communication, emails are still a crucial part of the way brands communicate with consumers. Just consider that in 2017, global email users amounted to 3.7 billion people, and, this figure is set to grow to 4.3 billion in 2022.

While this sounds conclusive, in practice, email marketing remains a hotbed of discussion with marketers divided on one question: Is the content or design more important?

The first email was sent in 1971. Back then, content ruled supreme. But over 37 years, emails have evolved to offering greater scope for marketers. Today, we want to focus on design. This isn’t just about finding the right images and colors for the message, but rather the opportunity to be fully interactive, enhance product engagement and have the look and feel of an app.

So how do marketers achieve that? It is by understanding that neither content nor design is more important. The performance of both is intrinsically linked to data. Only informative, relevant content together with engaging design will keep a customer’s attention. And whether it be an image change, text change or module change, data can now determine exactly how the customer interaction takes place.

Making the Leap

In 2018, email design is all about dynamic content.

Imagine an email campaign that automatically adapts based on the known interests of each subscriber. That is dynamic content. It is an advanced personalization technique, which uses data held within each subscriber’s profile to automatically display content more closely aligned with their known interests or preferences.

The key is aggregated data that can be pulled from email sign-up preferences, profiling subscribers or behavioral data. And dynamic content can be used in a wide range of applications. Either data-based design, including a CTA offering based on previous interactions, or using the nearest store location information or gender specific information. Or you could provide contextual content pulled from custom feeds, social networks, and website behavior. This information includes recent interactions, weather feeds, location, and customer persona.

Putting it into Action

This is all well and good, but you might be thinking, "We just don’t have time to create great designs and tap into dynamic content. We are always working to extremely tight deadlines.” The answer is templates and planning.

Modular email templates are the key to successful use of dynamic content. The template is a framework that includes a header and footer with several content modules. These content modules can be stacked and then removed, rearranged, and repeated in numerous configurations. Most importantly, they allow for design freedom without the need for extensive HTML knowledge.

As a first step, marketers should conduct an audit of previous email campaigns to see what works and how consumers reacted to design. Then, template the most commonly used modules and template dynamic content rules.

Ultimately, brands and marketers don’t have to compromise on email design to send a relevant, data-driven email. Why? Data drives design. It ensures that content is shown in the best possible way to different audiences. Relevant content increases familiarity and loyalty, which in turn, improves click through and conversion rates.

Want more?

Watch the webinar, Bridging the Gap: How to Orchestrate and Personalize the Entire Customer Experience, to learn how your organization can unify email and web orchestration with a strategy that will keep communications relevant and audiences engaged.

Watch the webinar today.

GDPR: Why CEOs Need to Lead from the Top Down

Oracle’s Alessandro Vallega discusses the need for cultural change to ensure GDPR compliance, and why that change must come from the top.

GDPR is now in effect. (Companies across every industry have been under pressure to become compliant since the law was introduced in 2016.) Some responded by changing their IT processes, others placed the burden on their legal team, but others only began to adapt in earnest just before the May 25 deadline was approaching.

Data protection must be treated with the right level of gravitas. It might be tempting to think you can steer clear of regulatory issues as long as you are not doing anything untoward with people’s personal data, but this is short-term thinking. GDPR may only mark the beginning of a global regulatory push to improve data protection, and regulation will only become more demanding.  

Real change requires a shift in culture. The way companies govern data has not yet caught up to the way employees use technology, which is why we still see staff taking a lackadaisical approach in many organizations. They save company information to personal devices, use (and sometimes lose) business laptops on the train, and turn to file sharing sites to share sensitive information. All these practices pose a security risk, and they are all too common. 

The cost of not complying with GDPR can be significant. Business leaders will be aware of the potential risk of non-compliance (up to 20 million euros or 4% of the company’s global turnover) but there are less obvious consequences too. Data breaches must be made public to the supervisory authority within 72 hours once a company becomes aware of them, and the reputational damage that comes with these if the company does not have a good handle on security, has its own cost.

In addition, a supervisory authority has the power to impose a temporary or definitive limitation including a ban on processing, and data subjects have the right to bring claims for compensation.

This makes GDPR a boardroom issue, but this does not mean companies can just appoint someone to take charge of compliance and let them run with it. With an imperative this important, the bucks stops with the CEO.

Business leaders must be figureheads for data protection. For an organization to manage data more responsibly and stay on top of its data in the long term, it needs buy-in from all staff. Each individual must be accountable for their actions and play their part in compliance, and this understanding must be driven from the top down.  

How can business leaders help achieve this? The first step is to make training compulsory. This could include anything from data management training, to workshops on protecting data or even running phish-baiting tests to help employees identify suspicious emails.

Incentives also help drive change. Data protection needs to be as much a part of someone’s job as doing their timesheets, so why not reward team leaders who have ensured all their staff have taken the appropriate training, or include security training as part of employee performance objectives? It will ultimately come down to HR, IT or legal teams to develop these initiatives, but the imperative must come from a company’s leadership.

For more information on GDPR and its implications for leaders across the business, check out our GDPR hub page.

Identify and Rejuvenate Your Inactive Customers

By Gabrielle Tao,VP, Product Management, Responsys Development, Oracle

As a marketer, you most likely have a list of your “inactive” customers. The definition of inactive customers varies from marketer to marketer. Maybe they’re customers who haven’t purchased in the last year or those who haven’t responded to any marketing messages in the last 180 days. They opted in to receive your marketing messages, but they don’t interact with your brand any more. It is a bit of a vicious cycle – your messages don’t engage them, but without their interactions, you are very limited in what you can do to personalize their marketing messages. This leads to even less likelihood they will engage and higher likelihood that they eventually opt out altogether.

There are two things you can do to further understand your inactive customers. With better understanding, there’s an increased chance that these customers will reengage with your brand.

Analyze Past Behavioral Data

This may seem obvious. But the truth is that a lot of marketers don’t do this or don’t do it often enough. These could be two key reasons:

  • Engagement data may be scattered among different interaction channels. As an example, if you have an email system that’s separate from the mobile system, or you collect sales transactions in multiple channels differently, your engagement data will be scattered.
  • By definition, inactive customers are those who have not had interactions with your brand for quite some time. Many interaction systems do not store interactions data beyond 90 or 180 days. So, to relate your inactive customers with their past behavioral data beyond the last 90 or 180 days, you will need to cross multiple systems.

Of course, it’s possible that analysis of inactive customers can be done with some system integrations. But the ease with which it can be done will determine whether this is a one-time effort or something you can benefit from continuously.

When you have a system to analyze the past behavioral data of your inactive customers, you could potentially answer these questions:

  • Who are truly inactive customers across all channels?
  • Did they ever interact with your brand after the initial sign-up? If not, you might need to ‘re-acquire’ them.
  • When was the last time they interacted with your brand, and on which channel? What were they interested in?
Append Third-Party Demographic or Behavioral Data

Customers who do not engage with your brand generally fall into these categories:

  1. They do not engage with any other brand either (deceased, invalid email, etc.).
  2. They engage with some other brands.

Appending your inactive customer list with third-party data can help you better identify category they belong to. Third-party data (e.g., demographics or behavioral) generally are gathered by third-party data sources and do not include customer interactions with your own channels.

While third-party data marketplaces have existed for decades, marketers have not been able to easily take advantage of them. That’s because most third-party data providers price data based on the list size, which isn’t optimal for specific use cases such as inactive customers.

As a marketer, the third-party attributes you need for your active and inactive customers may be different. For example, if you’re an email marketer, you primarily need to reduce the risk of bounces if continue to email your inactive customers. In this case, a different kind of third-party data attribute is needed than when you need to improve email personalization for your active customers. But because of the way third-party providers price and append data, you will need to split your customer list into active and inactive to get two separate appends, which in turn requires you to have two separate data contracts.

By following a very simple process to append third-party data without all the time and costs for signing data contracts and transferring data back and forth, you can understand your inactive customers more effectively:

  • Which of the two above categories have the majority of your inactive customers?
  • Will you risk getting more bounces if you send more emails to your inactive customers?

In part two of this article, we will take a look at an easy way to use the new Oracle Responsys CX Audience solution to learn more about your inactive customers.

Want more?

Download Customer Experience Simplified: Deliver The Experience Your Customers Want to learn how to craft an outstanding experience for your customers.

When Did Data Become So Valuable?

By Neil Sholay, VP of Digital – EMEA, Oracle

With GDPR compliance now mandatory, European businesses are razor-focused on their data protection measures, and with good reason. The regulation brings some long-overdue structure to today’s data economy, and the public is clearly eager for greater transparency into how their information is being used.

How did we get here? When did data make the leap from static figures in a spreadsheet to one of our most valuable sources of capital?

Just as prospectors once crossed continents in search of gold, companies today spare no expense in collecting as much data as possible to inform their decision-making and grow their bottom line, and it all began with the personal device revolution. It’s been just 11 years since Apple brought smartphones into the mainstream, but most of us can’t imagine life without our mobiles. The average person now checks their phone every 12 minutes, and in China, smartphone usage is set to overtake the time people spend watching TV.  

When we touch our mobile screen more than 1 million times each year, we create an enormous collection of data points. This information may seem innocuous, but it all adds up to an invaluable breadcrumb trail of insight that helps companies to better understand customers and target them with more personalized services.

Take Caixa Bank, one of Spain’s largest financial services companies, which uses its customer data to maintain a centralized view of how people interact with its services in-branch, online and on their mobiles. Established banks are fighting off competition from a range of new all-digital challengers, and this deeper understanding of customers helps organizations like Caixa Bank deliver the best of both worlds: a strong heritage in the industry and more tailored digital services. 

The same is true in the telecoms industry, where operators such as Telefonica have implemented an analytics program to better understand how customers use their services. By drawing on this insight, Telefonica can tempt its subscribers with additional content and propositions that are specifically tailored to their preferences.

Businesses are also looking more closely at their internal data to work more efficiently and cut costs. After centralizing its operations data, the UK’s National Health Services Business Services Authority began to spot and address inefficiencies that helped it save more than £580 million in just two years.  

For many organizations, the real value in data lies in predictive maintenance, the practice of pre-empting the failure of a product before it leads to major issues or customer discontent. For instance, today’s connected cars relay vast amounts of performance data to dealerships and car manufacturers such as General Motors. With this information to hand, companies can instantly alert drivers of any urgent issues that needs servicing before these cause any inconvenience or danger.

Never have businesses had so much information on their customers and prospects. It began with the smartphone, but everything from our cars to our kitchen appliances now contribute to the digital breadcrumb trail, and this is only the beginning. Businesses will continue to uncover new ways of collecting, combining, and extracting value from the information we provide.

As Oracle’s Paul Sonderegger told the Economist, “Data will be the ultimate externality. We will generate it whatever we do.”

This digital gold rush has already been a boon for businesses and consumers alike. All the mobile banking apps, food delivery services, and messaging platforms we use each day work so well and seem so personalized to our needs only because the companies behind them collect and act on user information to develop their offering. GDPR will by no means mark the end of these services, but it will bring balance to the relationship between organizations and the people they serve.

Many companies will be tempted to treat new regulation as a burden, but as with any change, it is also an opportunity. GDPR compliance is a short-term goal, but by rethinking their data management and security processes for a more open and transparent customer dynamic, businesses will ensure they can cope with evolving regulation in the long-term while giving consumers the confidence that their data is being used responsibly. 

To paraphrase TechUK’s Sue Daley, who joined a panel of data experts to discuss GDPR on the Oracle Business Podcast, we are moving to a world driven by connected devices, greater automation, and new forms of artificial intelligence. To succeed with these technologies, businesses will need the public to trust their approach to managing data.

Indeed, in an increasingly interconnected world we will see businesses shift from traditional security tools to risk and trust management, and now is the time to build a solid foundation for this more trusting future.

Click here to learn more about GDPR and discover how Oracle can help.

Also read 5 Steps to GDPR Compliance - It's Not Too Late to Prepare

Silo Busting is Essential to Delivering Personalized Experiences

This article part of our series on customer experience where we focus on topics relating to connecting data, intelligence and experiences. Further reading: Segmentation Must Be Connected to the Data and Technology Stack.

Digital technologies have dramatically improved the experiences of consumers, making it much easier for them to find what they want and to be provided with the service levels they expect. Their product and channel choices are greatly improved, which allows them to act at their own convenience.

Yet, rather than this satisfying the contemporary consumer, the opposite has happened. Customers’ expectations have accelerated, fueled by the very improvements in customer experience that digital technologies provide.

Is it any wonder then that so many companies are failing to deliver the seamless, excellent experiences customers demand as their basic expectation?

Data Silos Breed Chaos

The culprits, in many instances, are the brands themselves and their unwillingness or inability to break down organizational and technological silos within their own companies.

Data silos occur because businesses grow and change over time without a plan on how to manage their data and because separate teams inside or outside of a business don’t always work in a consistent way.

In a report called Culture for a Digital Age, authored by Julie Goran, Ramesh Srinivasan, and Laura LaBerge, McKinsey & Company identified functional and departmental silos as one of the most crucial digital culture deficiencies companies face.

“Each obstacle is a long-standing difficulty that has become more costly in the digital age,” wrote the authors. “The narrow, parochial mentality of workers who hesitate to share information or collaborate across functions and departments can be corrosive to organizational culture.”

It is just as damaging and corrosive to the relationships brands have with customers.

No wonder analysts like Gartner say the majority of companies are diverting money into data programs this year.

Oracle digital CX evangelist Mark de Groot says, “In our research, Next Generation Customer Experience: The Death of the Digital Divide, we found that a significant number of customers aren't impressed with the digital experiences brands offer.”

The authors of the report, which surveyed 7000 people in seven countries, were blunt in their conclusions, “The cost of failing – being slow, unresponsive, unavailable or incapable of adaptation – is brutal. Customers today have higher expectations. And when disappointed or frustrated, they leave. (In the case of the millennials, they don’t even bother to say goodbye.)”

The only way to overcome the problem of fragmented experiences is to take control of data.

Cross-Channel Challenges

Marketers understand that one of the biggest problems they face with cross-channel marketing is understanding customer interactions across those channels.

But often they lack access to cross-channel analytics making it hard for them to improve performance. They also find it difficult to track KPIs across channels.

Ultimately, though, until silos are tamed, it is almost impossible to build a usable unified view of the customer’s complete relationship with the brand.

Take the healthcare sector as an example.

Gartner Research Director Mike Jones said that one of the most common objectives of the healthcare sector is delivering a birth-to-death digital health record for patients.

While that may sound simple, the reality involves very serious complexity. “Bringing information from many different healthcare systems [that have] different structures, different data formats, different approaches to sharing and governance is extremely problematic to deliver. But without that the rest of the objectives almost become unachievable.”

In more than half the programs Jones studied, organizations were focused on four objectives:

  1. Patient ownership of data
  2. Big data and analytics platforms
  3. Open architectures and open standards for interoperability
  4. Developing new citizen services, which could allow online access to records

Each of these objectives can only be satisfied once organizations have their data stories aligned.

The smart application of technology can unify data silos for the benefit of all teams and partners. Oracle’s strategy is to acquire best-of-breed technology and then use our significant development experience to integrate them.

The win for our customers: rolling upgrades that add features, fix issues and speed tasks up. Then for their customers: seamless personalized experiences that build trust and confidence in the brand.

Two-thirds of Consumers Use More Than One Channel When Shopping

Segmentation Must Be Connected to the Data and Technology Stack

This article is part of our series on customer experience where we look at how to connect data, intelligence, and experiences. Read the previous article, Great Customer Experiences Rely on Robust Identity Management.

Your ability to deliver personalized customer experiences across multiple channels is increasingly being tested. There is a good reason for this – cross-channel communication is more effective, harder to do, and demanded by your customers. It's a fact: Targeted campaigns deliver much higher conversion rates than batch-and-blast efforts.

Unfortunately, the foundations upon which these experiences are built – data, segmentation, and the insights they generate – are too often poorly designed and even more poorly integrated.

For example, data is often trapped in silos, preventing any linkages and the insights this can produce. The ultimate result is incomplete or poor segmentation and, in turn, disconnected customer experiences.

If either the data or the intelligence applied to it are lacking, the result will be substandard. That is because disconnected data combined with disconnected intelligence produces disconnected experiences.

Breaking down organization data silos is the first step, but it’s not an easy one. This is particularly challenging for organizations dealing with complex or outdated legacy systems. However, success on this front creates a valuable resource for business and one that can underpin targeted marketing campaigns.

Of course, data alone lacks utility. The question for marketers is how to pool the data, and then create useful insights, which can be turned into appropriate messaging for customers and prospects.

Marketing 101

Segmentation may sound like marketing 101, but it is a process that still trips up some marketers. Effective segmentation is foundational to any marketing or advertising strategy and spans a range of tactics and meanings.

At a simple level, it is essentially labeling groups of people based on behavior, demographics, marketing tactics, and personas.

The temptation is to segment once, establish your customer labels, and then move on to insights. But effective segmentation is a continuous, iterative process that enriches your data over time. That also means segmentation must remain attached to the data and the technology stack.

The trap many marketers fall into is slicing their customer base into labels without connecting the technology stack. While the labels may be based on sound data insights, failing to tie them back to the data creates problems as it is unclear what characteristics make up the labels.

This is known as aspirational segmentation, and it can create problems when it comes time to execute. For instance, you might find that when you run a campaign to a certain segment you can't actually find that group of people because they are not connected to the technology stack.

Let’s say your target group is ‘fun-seekers.’ Marketers need to know exactly which attributes or data points qualify a prospect as a fun seeker. Do they frequent music festival sites? Listen to a particular genre of music? Or perhaps take advantage of travel promotions? However marketers wish to define it, knowing this allows for more effective targeting, but also fast tracks increasingly automated messaging.

If the funseeker segment isn’t attached to data, it's almost impossible to target, and without data it is also subjective.

And getting it right will give you foundational knowledge of your customers, which can be leveraged in effective data-driven marketing.

If segmentation is data-driven and data attached, then it can be executed upon and it's part of the technology stack. So not only can you campaign, but you can also run reports. Then, you can test and tweak it, and ultimately optimize the campaign.

Segmentation means lots of things. It can be as simple as selecting groups of males or females. More data complex segmentation might involve merging multiple data sources and using data science and machine learning to build a customized segmentation system.

The important thing is segmentation remains attached to data and becomes executable.

Marketers should aim to segment all their data, but it doesn’t have to happen all at once. Start small, segment the data you have access to, and see what works. Expand when you see the results. The most important thing is to get started!

Want more?

Download our free Busting 5 Common Myths of Marketing Automation to learn how the right segmentation is the start to proper predictive analysis, account-based marketing, lead nurturing, and attribution modeling.

Busting 5 Common Myths of Marketing Automation

Great customer experiences rely on robust identity management

With an ever-growing demand from consumers for personalised engagement, it is becoming increasingly important that marketers appreciate the importance of identity management as a piece of bedrock marketing infrastructure.

Put simply, without effective identity management, there is no ‘People Based Marketing’. In fact, identity management is the foundational component of concepts like cross-device identification, multi and omnichannel marketing, and the all-important ‘Single Customer View’.

These days all marketing and advertising technologies rely on successful identity management to enable application integration and data sharing.

But what is identity management?

It is a descriptive term that means the ability to link identifiers to a single person or household. Those identifiers could be a marketing channel, a database key or a device such as mobile phone or web browser.

For marketers to execute a consistent customer experience strategy, they need technology that understands and measures how consumers interact with their brands. For instance, they need to identify that a customer has visited their website, opened an email or gone into the store.

And to deliver personalized experiences across any channel, marketers need technology that can uniquely identify each user and their personal profile.

In both these use-cases, marketers will be well served if they have some technical understanding of the issues.

Targeting and suppression

There are two key improvements for a brand when they implement effective identity and access management: better targeting and better suppression.

Better targeting increases the ability to convert prospects because the brand can communicate with the person in more channels such as connected digital direct channels like email with paid media channels like display retargeting. This ability to reach a person across more channels with the same brand message increases the opportunity to convert and capture a greater share of wallet.

Conversely, the ability to suppress customers from acquisition strategies is one of the premises on which data management platform return on investment cases are built. Implementation of identity and access management in paid media channels allows a brand to stop messaging to loyal customers or hot leads.

So what might create identity and access management concerns at a technical level?

Start with your technology infrastructure.

Software as a service based marketing and advertising technology has revolutionised marketing. Marketers can now focus on doing what they do best - creatively and effectively telling their company’s stories while leaving technology companies to worry about the mechanisms of keeping the infrastructure up to date.

Cloud-based models also mean that brands benefit from the economies of scale of the vendors and from the continuous upgrade cycles and security management practices of their SaaS providers.

And for many companies, the ability to manage costs on a monthly or annual subscription basis de-risks technology choices as it removes lengthy and often hefty capital expenditure cycles.

However, there is a problem. In large enterprises, companies will have multiple SaaS suppliers and those suppliers might well provide technology to computers on-premise as well as in the cloud.

Identities and access need to be federated across all these environments within a Trust Fabric or else they risk creating data ghettos housed in isolated silos.

Applications come with their own identities. There needs to be seamless and very fast integration from one SaaS environment to another, from SaaS to on-premise and between different applications whether they be SaaS or on-premise and it can’t matter if it’s Oracle or non-Oracle.

To address this problem, Oracle created an integrated portfolio that provides the ability to drag and drop without coding. This means companies can do things like build federated integrations between a Salesforce or SAP system and a SaaS application like Oracle ERP Cloud.  This makes it very easy to do these integrations so that now you have your identities and access, federated in a consistent manner across the on-premise and cloud divide.

The problem - in terms of the customer's experience is that as each of these applications comes with their own identity, brand, and risk offering, resulting in a disjointed experience as they do not have the full picture of the client relationship.

And there is a wider issue than simply single sign-on both for customers and for staff.

Without the proper identity and access management, it is difficult to understand what is really going on inside your systems, e.g. who has access to which resources with specific entitlements and how that access is continuously reviewed and verified.

Getting insight into user behavior is an important consideration and then that needs to be connected to security systems. So if I see the inappropriate behavior I can step up authentication or I can turn off access via Multi Factor Authentication and User Entity Behavior Analytics. The outcome here is a reduction in the Mean Time to Detect and also the Mean Time to Respond in such a case of Anomaly Detection.

The reality is that information systems are complex and brands want to recognise their customers across multiple clouds either on their own premises or delivered by a SaaS vendor, and they need to manage identities across applications from multiple and often competing suppliers within a Unified Identity Trust Fabric.

And for the customer, this needs to be quick, easy and seamless.

Want more?

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Do marketers really want to be data scientists?

It is a challenging time to be a marketer. Consumers demand more personalised experiences than ever, and for marketers if feels like the customer's expectations just keep on rising.

In such a complex business environment a sophisticated approach to data is the key to success.

Increasingly, however, CMOs hear from software vendors, analysts, and the business press that it is not enough to be a great marketer these days. In an era of data-driven marketing, they now need to be a data scientist as well.

It is worth asking, whether that is really true or even practical.

Look around you at your peers in the marketing department. The reality is that your creative director needs more freedom to create. They don’t really need to program in Python or R. Likewise your field marketers do not need a technical specialist’s deep grasp of algorithmic concepts in order to put a qualified lead into the hands of the sales team.

Swings and round-abouts

We have been down this road before.

Think back a few years when marketing technology really broke into mainstream business conversations. Marketers were told then they needed to be technologists who understood IT almost as well as they understood their own core disciplines.

It didn’t happen because marketers and IT specialists started working in a collaborative fashion, each bringing their own unique expertise to the conversation. The IT department provides a robust infrastructure while marketers use the tools to ensure the right messages reach the right customers.

And data scientists? They ask questions of the company’s data and run experiments to help Marketing maximise the effectiveness of their campaigns and understand the responsiveness of their audience.

Marketers have always known that the better they understood their customers, the more tailored the experience and messaging they could provide.

So what is compelling the closer relationship between marketing and data science?

Changes in consumer behavior and the competitive landscape demand and technology have enabled, a shift from a waterfall approach to marketing where one campaign starts as another one ends, to a more agile campaigning.

These always-on campaigns driven by programmatic technologies let brands adjust on the fly as the data informs them what is working and what is not. (These are the kinds of questions real data scientists are great at asking!).

In this new method of marketing, brands look for the data they need to make a difference. That is the only data that should inform their marketing campaigns.

Take an everyday example.

Think about your favourite barista who sells you your coffee each morning. They may have read studies on the conversion rates with men over women, compared who has an IOS or Android phone, and they may even know your geolocation, your sex, and an estimate your age. But all of that is less relevant than the two most salient data points required to satisfy your needs as a customer -“What’s your name and what type of coffee do they drink!!”.

In the rush to prove their data-driven credentials too many CMOs are actually making their own lives harder. Research last year by Capgemini and the MIT Initiative on the Digital Economy called “From UX to CX: Rethinking the Digital User Experience as a Collaborative Exchange” clearly suggests that many brands are still trying to hoover up every piece of information they can gather without any regard for how it is used.

Capgemini’s Senior Vice President & Global Practice Leader Digital Transformation, Didier Bonnet suggests too many brands are missing an essential point - consumers want simple value exchange - a good product (associated with nice, easy experience) in return for their custom.

Marketers today can collect so much data on their audiences but the key is to focus on getting access to data you need. For instance, if you are in retail you don’t need all the transactional data, you may only need the last 5 transactions under 90 days old. Similarly, in financial services you don’t need all the call center data or branch data, you just need the last 3 activities over the past 60 days.

So how might this play out in the real world?

A financial services institution might, for instance, implement a simple approach with both new and returning customers where they inform them of their last five conversations across all channels.  That way the customer can see the last thing they did on the website, or a call they made to the call center, or what they did on their most recent visit to a branch.

If the company can deliver that information in near real-time into the campaign platform for next interaction, or back to the call center or branch staff they will be better placed to meet the customer needs.

Likewise, imagine the value to a salesperson in a retail store if they could see the last five interactions the shopper in front of them had with the brand.

Marketing is made better and more effective when great data is sensibly applied to meet the needs of consumers. That doesn’t mean the CMO needs to be a data scientist. It just means they need to understand which data helps them best understand what really matters to the customer.

Download the Data Driven CMO to skillfully learn how to decipher, understand, and leverage the abundance of available data to engage with customers: