A New Beginning

Let’s begin by defining “passion”. The root of this word is Latin, “pati”, which means “to suffer”.

Surprised? I was, when I learned this definition. The meaning of “passion” as we know it today is:

“An intense or extreme affection, enthusiasm, or interest for an object or concept.” (https://www.merriam-webster.com/dictionary/passion)

When someone asks you “What are you passionate about?”, remember that it is that one thing that makes you suffer, and that you enjoy the suffering that it brings.

“Passion” explains my relationship with CRM. This subject has expanded over time and I aim to explore topics that are associated with it, in this blog. This is a place for sharing my experiences so it is based on my personal experience and, therefore entirely my opinion.

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Today, many upstream and downstream systems work together to orchestrate an end to end business process,  enabling the enterprise to streamline their customers journey.  CRM as a platform provides that unique, holistic view of the customer – as required by specific user roles, across the enterprise and is centered around providing a “Single View of the Customer” to the end user.  “Single View of the Customer” or “360-degree view of the customer” is an old concept and is used by several CEO’s or senior advisors as the ultimate CRM objective. However, I believe each user needs to see a “Single View of the Customers Truths”, this would enable the user to have the right perception of the customer, based on what is needed for their role.  Nevertheless, CRM has become the platform for knowing the customer and streamlining processes for each user’s role across the business. This coupled with a simple user journey, drives customer experience and aids adoption of processes built around a customer centric operational model. Whether the business is looking increase productivity or drive customer experience, CRM as a tool, can be designed to deliver both sides of the coin, customer journey and user journey.

I started working with CRM in 2005. My first role was with Siebel CRM as a support analyst.  I was surprised at the number of applications the company developed in-house for simple functions like, bulk account reassignment, address change and task management all aimed at simplifying the job for administrators of the system. Although, these applications did improve the productivity for the system administrators, but they did not add any value to the everyday user of the system, or the customer.  Since then, my career journey has taken me through several products and projects, today, technology is moving from a CAPEX  to more of an OPEX operating model. 

Through this journey working with different organisations, I have become more familiar with CRM as a concept, and understand different project methodologies used to deliver a successful solution. I understand the importance of sound project governance, project methodology, agile principles and the importance of good communication structure and good change management in any CRM program. In my opinion, a “good” implementation of CRM leaves room for improvement, scalability, innovation and at its core is follows the “KIS” theme a.k.a keep it simple. An “acceptable” implementation  is mostly aimed at solving current challenges, building structures and models to support the immediate requirements.  Hence, a bad implementation is what was termed “acceptable” a year ago by the business.

My CRM product portfolio includes Siebel, Sage, Goldmine, Oracle and now I am predominantly focused on Microsoft CRM now known as Dynamics 365.

This experience, complemented by the functional and deep technical understanding of the product, has shaped the knowledge of CRM I have today. My experience encourages the belief that CRM is the heart of an organisation, it should enable business strategy and provide sufficient customer knowledge, to improve productivity and customer experience. I believe, CRM coupled with AI, IOT, and Intelligent Automation can add value to any organisation and this drives my passion further today then when I first started as a support analyst.

Data Management

As we grow more capability in the digital spectrum, our perception of this world, its vast properties and hidden truths are slowly being unravelled to our astonishment. The study of natural elements and objects in our environment, have led to the increasing need for us to store data to enable us to learn, build and recall information. Technology is now enabling us to also decipher patterns in our data to build objects and simulations to support theories that shape our understanding of this ecosystem, called “Earth”.

For example, the human genome project managed to store over 3 billion base pairs in a database. This, a result storing large amounts or data or “Big” data is used to build our knowledge of genes and help identify patterns that lead to a deeper understanding of genetically inherited diseases.  This impressive feat, accomplished through technology, is the driver towards a brighter future for our civilisation and Data lies at the heart of this movement. This technology led growth is now driving competition in the business world,  where we seek to answer questions that enable businesses to expand their boundaries and reassess their business impact as a brand or on a customer.

Today, technology firms like Google, Microsoft, IBM, Salesforce are competing not only on providing easily scalable, interoperable, cross platform technology solutions.  In fact they are also grappling with the challenge of applying artificial intelligence, to get to the underlying truths embedded deep within our data sets. Senior management in organisations, be it small, medium or enterprise, are waking up to find that the biggest obstacle to understanding and achieving optimum operational value is the lack of relevant data. More importantly, the risks and threats data security and information sharing are more relevant today than ever before.

Enterprises with multi-vendor models, need to adapt their mind-set to re-address the who, what, where, when, why and how of data architecture. To take advantage of machine learning an enterprise must now reflect on the source, intent, traits, confidence and consent for data processing. All these serve as collateral in forming a training dataset to build an accurate algorithmic model for machine learning and build next best actions.

Progressively, data-rich enterprises would need to address and strategically plan data structures and reflect on the impact, legality and intent of data content and storage.  Therefore, Data migration can not merely be seen as a sub task within our project plan every time an application is added to the enterprise repository.  It should be a welcomed and much needed exercise to restructure old, redundant and orphan data chains and business managers should make every effort to address data accuracy to fit the growing need for future automation.

Data migration is a part of almost every project, if done correctly it breaths life into an application and if executed poorly, can suck the life out of an innovative solution. The process of extracting data from one application and loading it to another application without disrupting or disabling active applications. This requires the following steps:

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The first step to data migration is extraction of data.  This is where you select a sub set of data in an existing system as active records to be transferred across to the new solution.  At each of stage of data migration you are likely to encounter issues and gaps in your database. To give a better definition, I have outlined some of the traits:

  1. Null Field and empty field
  2. No dates to show when the record was last updated.
  3. No audit trail of where the data came from, who changed it and why.
  4. Dates stored in an invalid format, without controls or validation in place.
  5. Required fields filled with “.” or “ ”.
  6. Incoherent, unnecessarily complex and redundant table structures.
  7. Lack of a documented or referenced data model.
  8. Fields that hold random data, which does not inform or add value to the user’s operational task and business reporting.
  9. Random and unstructured field codes.
  10. Lack of a defined data owner
  11. Lack of a data policy
  12. The mix of null fields, free text with no validation or format, incorrectly formatted dates, empty fields, complicated joins, required fields filled with ‘.’
  13. No field validation
  14. BI Reporting is an after thought

Data migration is not just about dumping data from one system to another. It is also not just identifying a cut over date and time. This topic spans the understanding of data as it adds value to your organisation, and should align with a strategy to transform existing data to a “to be” optimised solution. I find a lot of companies tend to throw technical consultants at the helm of data migration process and hope for a database administrator to provide the right data model. Whilst structuring of the database is a technical task, the business must define the purpose and value add for the information which is being stored or migrated.

As we learn more about these and other issues surrounding data, we should actively begin to build a framework which supports a regulated, GDPR compliant and efficient data strategy.

Social Listening

I want to say boring and old. However, that would mean I am blind to what is happening around me. Seriously! Companies who do not yet have a social presence or a Customer Collaboration Team (“CCT”), must be trailing behind. Without having proper channels you end up delivering a poor customer service and appear to be hiding behind the lack of an Omni channel presence. It is not a fad! It is here to stay, the bandwagon arrived and left already, with many adopters. Slow adaptors generally tend to get left behind.

Social listening is a strategy, it is not just tech, so please stop thinking that adopting a CRM system with social listening ability is the solution.  However with, Social Selling Assistant,  Dynamics 365 makes that journey easier. This app will help you grow your network, sell more and keep your customers engaged throughout your product lifecycle.

Intelligent Cloud

Web 2.0 introduced the concept of the user as the publisher, enabling rich content collaboration such as social media, sharing videos, blogs, and YouTube. It has made the internet a common platform for users to publish and share material. Nowadays the Internet has over a billion users, most of whom access it through their phones, visiting social sites. The Internet as a platform (#IaaP) is, therefore, the obvious disruptor in enterprise software strategy, making it a trend.

I feel all nostalgic! I remember Windows 3.1, just double-clicking on a shortcut to an EXE file to launch applications, Word and PowerPoint for instance. Hang on a minute! We are still doing that today on Windows 10. What has changed?

When it comes to progress, the shift has been mainly due to the introduction of virtual platforms, Cloud technology and the explosion of Big Data.  Cloud technology provides the necessary processing power to process significant amounts of data through ML or an AI engine. These advances are therefore facilitating organisations to make use of predictive analysis and machine learning functionality and by incorporating applications accessible from across the globe to orchestrate a connected enterprise.

Key players in the market are now competing on a different scale. Take Microsoft for example; it has shifted its attention to business application development centred on the Azure Cloud platform, utilising Azure Active Directory, #Dynamics365, ML, IOT, Power BI and Office 365.  However, Microsoft is not the only one to build Cloud based solutions. Amazon, Google, Rackspace, Salesforce and IBM along with several other are organisations that have taken to the Cloud.

The transition to and adoption of cloud technology has aided the evolution of Dynamics CRM 2011 to Dynamics 365. This transformation has been remarkable.

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Moving forward, hybrid cloud model is becoming a much-discussed solution allowing your organisation to switch between on premise, public and private cloud, maintaining your privacy and protecting your data online and across different providers. However, interoperability across different cloud platforms is proving to be a major challenge.

Given the above shift toward cloud technology, the question isn’t why we should move to a cloud model the more prevailing question is why should any software provider invest in keeping their solution on premise?

Data confidentiality remains the primary driver for on-premise implementations. Client side code, like JavaScript and AJAX, still leave room to exploit the client-server model, especially with the popularity of Web 2.0. This vulnerability creates room for data leaks and poses a risk to the privacy and confidentiality of data in most data sensitive and risk conscious organisations.

Internet Of Things

#IOT, perhaps best described as, a network of interconnected smart sensor devices, wirelessly sending data through the internet, signalling changes in states, which trigger an action or behaviour that will automate or add value to a process.

For Example, a car which is fitted with smart sensors or IOT devices, automatically warn the owner that the suspensions are not functioning as designed and sends a message to the manufacturer or a partner garage that its suspensions are not entirely safe. An alert will go out to a local garage CRM system, Dynamics 365 perhaps. The sales person managing the account will see the warning or alert. Owner will receive a call: “your car has just indicated that the suspensions are not in line with safety regulations, please can you bring your car in?” Alternatively, system will auto schedule an appointment and send an SMS confirmation to the owner for acceptance. I am sure having cars communicating directly with the service agents would add value to the service providers. Rolls Royce has taken advantage of this sort of technology for their jet engines.

Microsoft IoT Hub easily and securely connect your Internet of Things (IoT) devices and can send data to Dynamics 365. Dynamics 365 can then trigger workflows or display the required information in a dashboard.

This kind of automation serves well in any environment; however, the service industry benefits the most from adopting this application of technology.   A hive of interconnected devices driving some automated behaviour, or serving as a means of collecting data, can help build Smart Cities and Smart Nations. A world where systems can monitor our energy foot print, measure and reduce waste and improve monitoring of health issues, through to sending alerts to relevant providers to report on anomalies so they can be addressed in a timely fashion. 

Logic, though at the heart of decision making, is not the only major factor in making decisions. There are several influencing factors, such as political views, social norms and allegiance to groups which guide or impact the way we decide.  Internet of things, can provide information which can be an influencing factor in our decision-making process and reduce the amount of time it takes to implement or justify change.

The more we learn about our own intelligence and influencing factors that lead us to making decisions, the more challenges we address in building intelligent systems that can match or exceed our own abilities.

Automated Marketing

Marketing Automation refers to the creation of automated marketing tasks or workflow actions, to nurture customers and measure success.

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The main aim is to deliver a consistent and compelling message across all channels.  The adoption of cloud technology has provided corporations with the scalability and processing power required to automate and streamline marketing campaigns. Leading CRM systems nowadays have adopted this feature, either as part of their out of box functionality or through integration.

However, many believe that Microsoft Dynamics 365, as one of the leading platforms, still does not offer any marketing automation. Wrong!   Don’t believe me? Have a look.

Unfortunately, you do not need a degree in computer science to do this; all you need is a mouse. Drag and drop to build a complete marketing process. Simple!

Due to the growing demand for automated marketing tools, and cloud technology, as the platform, #Adobe and Microsoft decided to partner up. They aim to offer a robust solution, enabling consistent user experience and maintaining a joined-up enterprise.

 “What does this partnership mean for CRM?”  you may ask.

Microsoft is gearing up Dynamics 365 Marketing module by embedding features of Adobe Marketing. To this end, Microsoft Dynamics 365 is set to gain improved marketing automation features for CRM online.  Their joined effort may also bring, much loved, Adobe functionality like, the ability to generate and edit PDF documents along with e-signature feature, basically the entire content workflow accessible through Dynamics 365 is also a possibility.

#Dynamics365 combined with Adobe is set to have, the power to enable advanced segmentation, customer lifecycle marketing, lead nurturing, lead scoring, cross-sell, and up-sell improving customer retention, and marketing ROI measurement features.  Regardless of the features or integrations, Dynamics 365 has full support from both CEOs. Adobe Marketing Cloud has a complete set of marketing products which could enrich Dynamics 365.

 With these marketing functions, advanced analytics and continued use of intelligent dashboards, your organisation can bridge the gap between sales, marketing, and visualisation.  Moreover, with all these features embedded on a single platform, like dynamics 365, you can expect an increase in the rate of conversion and provide a single collaborative platform for users across the enterprise. As Dynamics 365 continues to challenge Salesforce, and other competitors, we can clearly expect investment, growth, and innovation in this area by all CRM providers.

Is this a trend or just hype?

While some organisations, recognise the benefits of Automated Marketing, they do not fully appreciate the dependencies which lie at the core of this functionality. Marketing is a strategy and needs to be established organically in an organisation. giphyMarketing strategy should encompass, a suitable model for generating leads, with a clear understanding of the target customer profile and measure sentiment throughout the customer’s journey. For this reason, automated marketing is just a tool to drive a consistent marketing message across the web, mobile, video, social, and other channels.

Advanced Analytics (Machine Learning)

Data has become a commodity. The more we store, the more challenges it brings.  However, one of the key strengths emerging from captured data,  is  what we learn from it.  Companies are surviving and experiencing continued, sustained, growth by applying the right tools to effectively manage the data captured. For instance, Dynamics 365 CRM “out of the box”,  meaning without any development effort, provides cross-selling and upselling functionality. It suggests a product, based on the product’s relationship structure stored in the system.  Hence, with applications like Dynamics 365, your agents will surely be asking: “do you want fries with that?”; shortly followed by: “do you want to go large?

On the other hand, Advanced Analytics,  is what puts your organisation leaps and bounds above its competition. To do this properly, you must have the right data set, and must also know the traits required to get the most value out of your data.  For example, Azure Machine Learning (ML) service, a part of Cortana Intelligence, can process significant amounts of prepared data, uncovering patterns and trends in your customer interaction history. To do this effectively, #ML uses techniques from statistical analysis to build a coded algorithm, called a Model. This model is used on data to identify traits and can be plugged into an application, like Dynamics 365, to provide some real insights.

 

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This form of advanced analytics, surfacing on your CRM system, delivers the right kind of customer-centred information, adding a new dimension to your decision-making process.

For example, banks may use machine learning to identify fraudulent transactions.  To achieve this, data is prepared to isolate traits such as location, transaction amount, time and type of transaction. ML  can analyse these traits from customers transaction history to build a model, that can learn the general trend or pattern of a normal transaction. This model can be  used to deduce the probability of a transaction being fraudulent.

#ML will not only increase the value to your business; it will also enable you to work more in line with your customers’ behaviour, understanding your customers’ demands  and reveal some interesting “Relationship Insights.”

 

 

Consider the amount of data harvested by companies like Google and Smart Phone providers, I shudder to think, what data do they hold? Moreover, What kind of insights can they gain about our behaviour, using this data? Who has access to that data and how are they sharing that information?

These questions raise further concerns around data protection,  data privacy and more importantly, challenge the authenticity for consent of data processing. This use of data also raises legal and moral debates surrounding data privacy, especially, when data is transmitted across international boundaries.