A maturity model for MarTech to help you know where to start

Marketing Operations and the MarTech stack covers so much today and it can be hard to know where to start upgrading. This is especially true if your marketing team is feeling pain across a couple of fronts and have jumped right to solution rather than articulate the business problem first. It's happened to all of us, we see a shiny new object... some case study the had "great" results and suddenly everyone want to copy it without really understanding it. You might find under some more scrutiny it's just someone peddling snake oil... so be sceptical.

Everyone's in a different situation and has different needs or areas that need upgrade. What do you plan to updated next?

What area does your company need to upgrade next?

• Planning & Budgeting
• Finding Patterns
• Data Source
• Match Content to Audiences

See All Options

I've expanded on the categories of Marketing activities / responsibilities by adding maturity levels to each area and a short description for each. If you can map out which maturity level you are currently at in each of the areas you may find an area which is lagging behind the others and needs attention. In the table below, each level is like a good, better, best (for now) type of categorization. I tried to set Level 1 to represent the average marketing organization and Level 3 to where most aspire to be in the future.

Area	​Level 1	​Level 2	​Level 3
Planning & Budgeting	​Centrally managed budgeting and spend	​Rules based logic / process for optimizing spend	​Predictive analysis for planning and ROI
​Finding Patterns	​Centralized data for analysis	​Build segments based on rules and select top performing content based on response metrics	​Predictive analytics to group look-a-like contacts and know what content work for those groups and at each stage
​Data Source	​Firmographic, demographic, 1st party activity	​Addition of Technographic and Intent data	​Addition of sentiment, 3rd party persona and profiling
​Match Content to Audiences	​Manual curation of content and list build based on simple firmographics/demographics (Batch driven comms)	​Rules automatically determine what communication they will get (Nurture driven comms by segment)	​Content recommended for individuals based on their role and consumption habits - from what we know works (through predictive analytics that update in real time)
​Building content	​Centralized content repository. Each system calls the content from a DAM	​Thoughtful content build process that targets segment / persona / stage	​Dynamically build content (building material in DAM) for each individual separately based on their role and consumption habits from what we know works
​Automate Tactics	​Each system's automated comms are managed separately and automation is not the default mode of operation	​Automated rules/comms are the default and batch comms are the exception	​User can select content types and cadences that our systems adjust for and dynamically selects appropriate content
​Coordinate Touch Points	​Not coordinated or only across some channels but response data is centralized and system/comm priority is centralized	​Coordinated across all channels... all displays the same content recommendations	​Coordinated but with context of most recent interaction and recomendataions are adjust
​Capture and Process	​Central intake and processing of responses	​Automate all list loads and rules/validations applied to records during intake of responses	​Framework of validation rules and network of enrichment vendors based on type of data… low cost to high cost)
​Prioritize Follow-up	​Rules based prioritization… not validated by statistics	​Predictive / statistics based prioritization that is self learning	​Predictive (more granularity for separate actions): Account fit, Account Activity, Person fit, Person Activity
​Support Sales	​Collect simple feedback of Marketing Qualified Prospects for future adjust to scoring models	​Collect detailed feedback of Marketing Qualified Prospects and detailed Contact Roles on Deals for future adjust to scoring models	​Coordinated activities (target account and persona etc)… Marketing lines them up/ Sales knocks them down
​Tactical Marketing reporting	​Campaign Response and Pipeline reported out of central data set and system	​Optimize by monitoring DB health, Content performance, Funnel health (create vs convert)	​Optimize based on Predictive persona/buyer role group (content consumption look-a-likes). Predict Funnel Conversions and any new activity needed to pull in future deals earlier and seed new future deals to replace
​Forecast / ROI	​Responders are the base unit monitored closely, data centralized in one system for reporting and modelling. Reports are self-serve. Rules based Attribution (campaign/pipeline)	​Account (more closely aligns to Deals) based metrics and KPI... Overlay outbound activity and spend. There is one set of approved reports that are the source of truth	​Buying group based metrics and KPIs… account for spend efficiency. Predictive base attribution (campaign/pipeline)

Which definitions need the most changing? What should level 4 look like? Share your insights in the comments below.