Digital Marketing Maturity Model
Would you say your company is leveraging at least 75% of the features
and functionality that your martech stack provides? No?
You’re not alone. Over the years I’ve noticed a pattern in adoption of
digital marketing tools. Many companies get to a point where they’re
using about 20% of the platform’s capabilities and then plateau. Why?
Reasons typically include:
· Lack of skilled personnel to successfully guide maturity
· Lack of time to focus on improvements
· Process limitations that might prevent maturity
· Data limitations/integrations that prevent maturity
Since
digital marketing is typically a significant percentage of the
Martech stack investment, it makes sense to work toward continual
improvement, stretching, exploring and leveraging all the nooks and
crannies digital platforms offer. But how? What should you focus on and
how do you measure success?
To that end, I’ve created the
following Digital Marketing Maturity model, which acts as a
high-level GPS guide in quickly identifying where you currently are,
what to focus on next and what success looks like at each stage. It is a
compilation of existing maturity models, together with my own years
of digital marketing consulting experience.
You’ll
notice its broken down into 5 maturity levels, from Crawl all the way
up to Best-in-Class, across 9 themes, including CX, Strategy, Targeting,
Data, Analytics, Sales Alignment, Tech, People and Process.
It’s
no accident that the very first theme is CX, since it is the end game
and overarching cumulative goal of maturing the rest of the themes.
Where to Start
Obviously,
there are many layers within each stage, but the best approach is to
begin with a high-level assessment of where you are now, within each of
the themes (columns). It’s important to get consensus on where you are
now from all those who will ultimately contribute to marketing’s
progression up the line. If the data team thinks you are at the RUN
stage and you think you’re at the CRAWL stage, good luck making any
progress. Misalignment is one reason companies get stuck in a stage to
begin with.
Making Progress
It’s important
to only focus on those activities that will pull you up to the next rung
for any given theme. Don’t try and boil the ocean and go from crawl to
BIC all at once. Take each rung one at a time. Once you’ve achieved it,
focus on achieving the next rung, and so on. You’ll have a much better
chance of success.
Invariably what prevents many from achieving
higher levels of maturity is data. Missing, inconsistent and outright
bad data will prevent most efforts to mature and certainly any effort to
improve CX. So, when I have conducted maturity workshops I also invite
data, CRM and IT stakeholders to the party, so they can collectively
identify and discuss potential blockers and solutions on the fly.
Another huge benefit is it also aligns support in understanding the
impact of improving cross divisional challenges, from a maturity POV.
Action Plan:
Here’s are the recommended steps to pull this off:
1. Schedule a Marketing Maturity Workshop (invite marketing, sales, CRM, data and IT)
2. Agenda:
- Introductions
- Goal of Workshop (identify and improve marketing maturity across 9 key themes)
- Review Maturity Model and get consensus on what level the marketing team is currently at across each of the 9 themes
- Review the next rung up for each theme and begin to identify solutions to get you there
- Create Project Plan and list of action items
- Execute
- Rinse and repeat
So
how long does it take to go from zero to hero? Well, almost no one has
the luxury of starting from scratch (bummer eh?). There are always
legacy platforms, data silos and other integrations that slow the
progress, so it really depends. A good general rule of thumb is one rung
per year for enterprise and 2 rungs per year for SMBs. I’ve seen some
companies go from crawl to BIC in less time, but the entire organization
was fully committed and supported at the executive level.
Use
this as a high-level roadmap in starting digital marketing maturity
discussions. It should help keep the guard rails in place, funnel the
collective focus, all while generating alignment and momentum.
Steven Kellogg
Crowds2Crowds
DMP, CDP, Data Lakes, Oh My...
A Data Primer for Marketers
As someone who started my professional career in Marketing, I along
with many of you have a love/hate relationship with data. Love in the
sense that having the right data is all-knowing and can bring you
wealth, power and fame. Hate in the sense that not having all of it, or
having it and it’s not complete, not accurate and/or not relevant.
We
all know customers fully expect a consistent, cohesive conversation,
from the moment they first meet a brand to their last interaction,
regardless of channel or device. To pull this off, marketers
require…well, you know, data.
The lines between marketing
expertise and data expertise continue to blur and may in fact already be
almost indistinguishable. Marketers are now expected to be data
scientists, or at least be able to have intelligent conversations with
those who live and breathe data every day. (BTW that's marketers).
If
you find yourself in this position, (and want to impress your
colleagues with your data chops) below are definitions and use cases for
common data platforms designed to get us marketers closer to customer
experience expectations.
CDP (Customer Data Platform)
The biggest difference between a CDP and other types of data platforms is that a CDP is used to manage customer
data. It is a data resource used by and typically controlled by
marketers. While DMPs tend to focus on top-of-funnel initiatives, CDPs
focus on mid to lower-funnel activities.
Think of a CDP as a
central repository of siloed pockets of customer data; things like
personal identifiable information (PII), transactional data, behavioral
data, explicit data, offline data, call center data, etc. that marketers
need in order to create specific customer segments or to drive 1:1
conversations. Additionally CDPs can also clean and dedupe this
information. The real magic is that it matches all these touchpoints
into a single customer record, holding and consolidating a customer’s
interactions across channels/devices. Lastly it can feed this
information to other platforms, like BI tools, DMPs and marketing
automation platforms. It can be a dream come true for personalization,
as well as customer insights, especially for those marketers currently
frustrated by data silos.
If you are shopping for a CDP, one
important point of comparison is the breadth of off-the-shelf
integrations each solution has. Make sure the solution can easily
integrate to your other data systems. Another point of comparison is the
extent with which these customer profiles are updated in real time.
These single customer records are useless if they are even a few hours
old.
There are a host of CDP point solutions on the market as well
as CDP functionality built into some existing enterprise marketing
automation platforms. If you are interested in learning more, below is a
list of CDP vendors to get you started. It is not an exhaustive list
nor a recommendation, but a good place to start additional research:
DMP (Data Management Platform)
First
of all, who came up with all these names? Data Management Platform,
Customer Data Platform - good grief. I suggest a new acronym: CONFUSED
(Customer Or Non-customer Frameworks for Unstructured or Structured
Enterprise Data). Not bad eh? It only took me 5 days to come up with
that 😊.
Anyway, a DMP is typically used for top of funnel
initiatives as mentioned above. While it can ingest 1st party data, it
focuses primarily on 2nd and 3rd party anonymous data. It’s great for
web display ads, retargeting, look-alike modeling, and other lead gen
efforts at scale.
Publishers also use DMPs to monetize their data.
Unlike CDPs that keep an ongoing historical record of activities, DMP
data is often kept for shorter periods of time. Basically you can model
the attributes of your best customers and have a DMP go out and find
more just like them. One important point of comparison is match rate. How effective are they at matching these attributes and stitching
together the identity dots across cookies, device IDs and IP addresses?
If you’d like a more in-depth DMP primer, check out my DMP Primer video I recorded back in 2016. It’s still relevant today.
Below
is a list of DMP vendors to get you started. It is not an exhaustive
list nor a recommendation, but a good place to start additional
research:
Data Lake vs. Data Warehouse
Data Lakes (as well as Data Warehouses) are both used to store big
data. However a Data Lake holds raw unstructured data, the purpose for
which is as yet undefined. Consider it a dumping ground for any data you
think you might want to use sometime in the future. A Data Warehouse
typically holds structured data that has already been filtered or
processed for more specific purposes. A Data Lake is used by Data
Scientists, while Data Warehouses are often used by the broader
business. Companies often need both, as each requires different amounts
of storage capacity and specialized tools to manage.
BONUS: AI vs Machine Learning
These two buzz words are thrown around everywhere, even in real estate broker ads. So what’s the difference?
According to the Artificial Intelligence Marketing Association, “Artificial
Intelligence (AI) is the emulation of human intelligence process by
machines be it a computer or an IOT device. The process involves
learning (extracting meaningful insights and patterns), predicting (use
the gathered information to help devices make plausible future
decisions) and self-correcting (the art of getting more efficient every
day). This is made possible by the three major wings of
Artificial Intelligence — Machine Learning (ML), Natural Language
Processing (NLP) and Deep Learning (Neural Networks). AI has established
itself in every business sphere around the world -encompassing
everything from Rule-based machine learning to visual classification.
Its applications range from object recognition to preventing high-end
cybersecurity threats.”
Artificial Intelligence
is what Tesla uses to drive cars autonomously. It’s what manages
intelligent stock trading systems. Here are some very interesting stats about AI, published by CMO.com
Machine Learning
is what the iPhone uses to blur backgrounds in Portrait mode. It’s what
virtual assistants use in their speech algorithms, (using NLP Natural
Language Processing). Waze, Google Maps, any of the driving direction
apps use lots of machine learning to find you the quickest way there.
All
in all, it’s a great time to be alive and enjoy all this cool
technology! Hopefully you now have a better understanding of these
terms. Now go impress your favorite data architect, or better yet, your
boss.
Steve Kellogg
Crowds2Crowds
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