MarTech Adoption – How to increase Marketing ROI

Beside your MarTech stack, MarTech adoption is a critical factor in driving success and high return on investment (ROI). Hence enabling MarTech skills and regular use of those skills is a critical first step. Skills come primarily from training and that needs resources especially investment of money.

After spending a lot of $$ on procuring MarTech, resources for training become a luxury that we cannot afford. Failure to train marketing teams on MarTech skills will create a huge barrier to MarTech adoption. If you are not planning to invest in MarTech training you are starting with a poor ROI from the beginning. I caution you against that and I hope you have both training and onboarding as part of MarTech strategy and investment plan.

“Organizations that invest in acquiring and maintaining MarTech skills are eight times more likely to also experience good or very good ROI.”

– A study I read few years ago.

Key Metrics to track MarTech Adoption

Some of the key metrics that come to my mind to measure MarTech adoption and ROI are:

  • Volume metrics: Measure things like impression, leads, form fills, product views etc.
  • Financial metrics: Measure things like opportunities created, revenue generated, revenue influenced or ROI. You need a stack that has data well connected for your insights team to calculate these.
  • NPS of Customer Zero: This is an ongoing tracking of satisfaction across your internal users. I have found this to be an important metric for MarTech adoption.
Customer Zero NPS is very helpful metric for tracking MarTech adoption
Customer Zero NPS – Leading indicator of MarTech Adoption in 2016 for MarTech
  • Productivity metrics: Measure things like time saved or requests handled, adherence to SLAs, time saved, the velocity of deals, speed to lead etc.
  • Stack Complexity/Retire Legacy assets: How many technologies that can be consolidated as we move from a disconnected MarTech stack with redundant technologies to a MarTech stack that is connected like a platform.

You may argue that first two are marketing metrics. MarTech teams should co-own these metrics as this ensures investments in solutions that enable business outcomes. This will also motivate your MarTech teams to integrate across technologies. Integration and Data are two key aspects in the evolution of MarTech maturity. I will be sharing industries’ first MarTech Maturity Model built with over 30 years of collective MarTech experience in the next few weeks. You will be able to use it as a framework to evolve your MarTech program.

Ideas to enable MarTech adoption

Here are some of the ideas that I have used or plan to use in the future to enable MarTech adoption and Marketing enablement:

  • As part of MarTech governance ensure your marketing stakeholder(s) are part of prioritization. They should also share responsibility for enabling ROI. Do this before you decide to invest in that MarTech solution.
  • Involve key marketing stakeholders in vendor selection, implementation and onboarding as a responsible party.
  • As soon as the marketing team onboards on MarTech solution, start creating monthly reports that use 2 or more metrics mentioned above to track MarTech adoption.
  • Roll out onboarding sessions followed by on-going training.
  • Track MarTech adoption with the help of the vendor and report out on a quarterly basis. Have a north star for adoption and request the vendor to come with recommendations to achieve it.
  • Roll-out a full-service digital marketing academy that works round the clock to enable. I will attribute the success of our MarTech program at McKesson to the Digital Marketing Academy. Here are some of the focus areas I recommend for a MarTech or Digital Marketing Academy:
    • On-going training on essential and emerging technologies
    • Certifications
    • Sharing of Best practices (internal and external)
    • Awards for best work in various categories
    • Opportunities to network across division if you are in a large enterprise
    • Have fun included in your program, so that various teams can bond across team silos
Marketing teams like to connect with each other
From Survey after 2016 Digital Marketing Academy at McKesson

I hope this helps and I will love to be part of your MarTech adoption and Marketing transformation journey. Feel free to share your experience on the topic of MarTech adoption and Marketing Enablement.

Feel free to reach out if you need guidance on putting together MarTech adoption strategy or establishing a Digital Marketing Academy.

DMP vs CDP vs DATA LAKE

During Customer Data Platform (CDP) and Data Lake discussions, I often come across wild assumptions on Data Lake’s ability to solve every problem while CDPs end up being heavily underestimated. In my opinion, neither CDP and nor Data Lake is replacement for each other but a perfect complement to each other. Data Lakes are a key source of data for CDPs while CDPs can help improve the quality and completeness of data in Data Lake. Another key platform in this mix especially for marketers is the Data Management Platform (DMP). I will like to spend some time to answer the DMP vs CDP vs Data Lake question.

Don’t think DMP vs CDP vs Data Lake but DMP+CDP+Data Lake.

If you don’t have the patience to read through all of this article on the difference of DMP vs CDP vs Data Lake, please skip directly to the summary section in the end.

DATA MANAGEMENT   PLATFORM (DMP)CUSTOMER DATA   PLATFORM (CDP)DATA LAKE
DEFINITIONData Management Platform (DMP) collects anonymous web and digital data. It comprehends information about prospects psychographics and demographics.  

Manage segments of customers with anonymous profiles.

For broadening marketing reach by building segments, audience mining etc.

Capture targeted audience at the right time in the buying funnel through relevant messaging.

Better optimization programs and smarter media buying decisions can be taken based on audience analysis and latest campaigns.
Customer Data Platform (CDP) is a type of packaged software which creates a persistent, unified identifiable customer profile that is accessible to other systems. Data is pulled from multiple sources, anonymized, cleaned and combined with third party data, intent data etc. to create a single profile of a customer.  

CDP enables real time activation of omni-channel experience across. CDP data can be leveraged in real time to provide more personalized content and delivery over web, mobile, Email, ABM, Ads etc. CDP data is accessible by external systems and structured to support digital and marketing team needs for experience management, campaign management, marketing analysis and business intelligence  

CDP is always a hot storage meaning easily retrievable and live connected to like customer master. CDP doesn’t need technical skills to manage and operate.
A Data Lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. You can store your data as-is, without having to first structure the data, and run different types of analytics—from dashboards and visualizations to big data processing, real-time analytics, and machine learning to guide better decisions.

The key focus of Data Lake is to ensure that highly connected data is available to all enterprise systems and functions.

Data lake can have a combination of cold and hot storage. Cold storage for more older data like over 3 years.  Data Lake need very technical resources to build and operate it. Data Lakes don’t offer integration with last mile solutions like MarTech solutions.

Data Lake provides ability to understand what data is in the lake through crawling, cataloging, and indexing of data. It always ensures data assets are protected.

Data Lake allows to run analytics without the need to move data to a separate analytics system. Generate different types of insights including reporting on historical data and doing machine learning where models are built to forecast likely outcomes and suggest a range.

Different types of analytics on your data like SQL queries, big data analytics, full text search, real-time analytics, and machine learning are needed to uncover insights. You can create new business models based on historical data and new financial models based on customer behavior, product categories, market data, risks and opportunities
 USERSAdvertising Professionals Ad agencies Marketing (limited)Digital Marketing Customer Experience Sales (limited)Data scientists, Data developers, and Business analysts (using curated data)
IT
Sales
Finance
HR
Marketing
Digital
DATA SOURCES & MANAGEMENTData is ingested   from various client and media sources like marketing analytics, CRM, ad-servers, publisher partners and point of sale (POS).
Data is also collected from mobile apps, client’s website, as well as other channels that use native apps.
It is then augmented and enriched with   3rd party vendor data; private data exchanges are established.
First Party Data:
Web Analytics
CRM
Advertising Data
Marketing Automation Data
Second Party Data
Third Party DataIntent Data
Marketing Lists
Device data
Etc.
Data Lake can connect structured and unstructured data available in:
MDM (Master Data Management) systems
ERP CRM
Commercial Data
Product Data
Multiple other critical backend IT and Data systems in an enterprise
Line of Business Applications
DMP vs CDP vs DATA LAKE

Summary and Recommendation on DMP vs CDP vs Data Lake

DMP-vs-CDP-vs-Data-Lake

So how should we use this information on DMP vs CDP vs Data Lake and apply it to your business? DMP can take care of most of your needs if you are only focused on marketing segmentation and advertising. There are many mature products in the market that you can buy and start using immediately. If you have use cases broader than that (I hope so), then you must look into CDP and Data Lake. If you are a CDO, CMO or CIO reading this, you must look into CDP and Data Lake. CDP and Data Lake are both required by every organization as both provide solutions to different problems. While Data Lake brings the data from enterprise together and makes is useable immediately, CDP focused on doing the same for the use cases limited to teams focused on the customer side. These are primarily digital and marketing teams.

If you have a functional Data Lake, you should build a CDP (light) as data lakes are not built to solve last-mile use cases. Your digital and marketing experiences will struggle as you will not be able to utilize Data Lake to full potential.  CDP implementation will be lightweight and primarily focus on

  • Append digital and marketing specific data that is not available in Data Lake.
  • Create customer 360 and build segments for activation
  • Connect with last-mile experience and marketing systems to activate the data

If you only have CDP, you should look into building a Data Lake to solve bigger use cases and enable digital transformation in other areas like Sales, Customer Service, finance etc. Data is the blood for a Digital Transformation. 

If you don’t have both, you should start at least with CDP as those can build quickly and you can start hacking growth while you build data lakes that can take years in a large enterprise. As I mentioned above, you will still need a Data Lake. Plan to have that in the long term.

The question of DMP vs CDP vs Data Lake is not right as all these systems come together and help you enable transformation in the digital age that we all call Digital Transformation.


More suggested content from some experts in this space

Some additional content from the post on LinkedIn where I got some good feedback from experts in this field:

Most organizations are looking to add CDP to their MarTech stack as they are getting a data lake stood up, leading to parallel efforts and often times the 3rd party CDP (with speed to market) beating the in-house data lake build initiative. Then it gets to be buyer-beware as all CDPs aren't true and good CDPs. It's all about having laser-focused CDP use cases ready to deliver business value and knowing which vendor to partner with to maximize ROI. - Fauzia Chaudhry (Senior Manager, MarTech, Robert Half)
Today’s customer is on at least 5 or more connected device at any given point in time and with this device hopping the expectation is to have the same intimate moments of delightful and seamless experience on all of the channel of engagement and that is where CDP makes an immense impact, especially with privacy and regulations. - Raphy Mathias (Domain Information Officer, Toyota Financial Services)
CDPs are designed from the ground-up to solve both of these problems using AI to make sense of the data, and automation to active data into individual channels. As channels proliferate and customers move to Digital consumption modes, this combination of activation + automation is a must-have to grow Revenue without adding complexity and cost. - Shashi Upadhyay (EVP, Dun & Bradstreet)

Problem Reframing

Reframe Problems Before Solving

I recently understood the importance of reframing the problem before looking into solutions. It helps in creating simple, effective and economical solution in far shorter time.

Problem Reframing
Image Source: HBR

Here are Seven practices for effective reframing of problem in few minutes that I learnt from HBR article:

  1. Establish legitimacy: It’s difficult to use reframing if you are the only person in the room who understands the matter. Share this article with your team https://hbr.org/2017/01/are-you-solving-the-right-problems
  2. Bring outsiders into the discussion: Someone who works with your team but not part of it. They will think differently and challenge the group’s thinking.
  3. People’s definition in writing: This helps in ensuring everyone have the same view and understanding of the problem.
  4. Ask what’s missing: This ensures the description of problem is accurate and complete.
  5. Consider multiple categories: Invite people to identify specifically what category of problem they think the group is facing.
  6. Analyze positive exceptions: Look to instances when the problem did not Occur, asking, what was different about that situation?
  7. Question the objective: Reframe by paying explicit attention to the objectives of the parties involved first clarifying and then challenging them.

Social Business

Social business represents a significant transformational opportunity for organizations. Many companies, after initial forays into external social media, are now realizing the value of applying social approaches, internally as well as externally. Social business can create valued customer experiences, increase workforce productivity and effectiveness and accelerate innovation. But many companies still wrestle with the
organizational and cultural challenges posed by these new ways of work. A new IBM Institute for Business Value study, based on responses from more than 1,100 individuals and interviews with more than two dozen executives from leading organizations, reveals how organizations can use social approaches to create meaningful business value.

-IBM Institute for Business Value

Getting your 100,000th “Like” on Facebook, or having your latest pearl of wisdom retweeted 200 times  is all well and good, but are these activities driving revenue? attracting talent and bridging the collaboration gaps in your organization? Is your use of social media allowing your organization to engage with the right customers, improve their online experience and tap into their latest insights and ideas? Does your social approach
provide your customer-facing representatives with the ability to search the globe for expertise or apply learnings?

These are some of the questions worth thinking.

 

Day 2 of MarTech Conference

Here are my tweet notes from Day 2 of Marketing Technology conference (MarTech) at Boston


Some of good reads from others who attended MarTech.

REVIEW OF #MARTECH, THE INAUGURAL MARKETING TECHNOLOGY CONFERENCE by CHRISTOPHER S PENN