Organizations often overspend on technology. They’ll fall prey to the hype from vendors and analysts due to their misunderstanding of the technology’s TCO and/or miss-aligned technology vis-a-vis communication strategy. Or sometimes, it’s simply a lack of clear communication strategy itself. This is written for organizations who seek a more holistic approach to marketing operations and the martech stack. Let’s start by breaking this down into manageable pieces before we get into the weeds. 

What is a marketing tech stack?

At the most basic level your marketing tech stack is all the digital tools you use for marketing; whether it’s software, web-based subscriptions, or hardware.

Why does it matter?

No matter if you are D2C, B2C, B2B, government, or nonprofit—any organization has to have a digital marketing presence to reach its target audience. The tools that make up your marketing tech stack enable you to maintain and grow your digital presence with more efficiency and efficacy. 

Why is managing your martech stack important?

  • Save money by managing subscriptions closer
  • Keeps things secure 
  • Continuity between team members 
  • Maintaining efficiency 
  • Helps create sustainable workflows

Where do things fall apart? 

Examples of mismanaged martech stacks: 

  • A fashion brand spends over $100k a year for an overpriced DAM system and does not consider the administrative overhead. They could be better served with BOX or Dropbox which costs a fraction and needs far less administrative attention.
  • A hospital chain makes a huge investment in a customer experience platform; better targeting being the big selling point for the investment. Yet the organization has no strategy for targeted content generation/curation or any plan nor dedicated resource for the needed data management.
  • A large financial services firm buys into a large multi-year “marketing cloud” agreement to drive personalization. But they don’t realize the compliance limits of personal data collection due to lack of experience and their failure to plan a personalization strategy.

What these examples have in common are failed expectations for technology to solve complex business issues. They all lack a well-defined strategy and are only supported by limited human intervention & administration.

The most critical aspect of managing marketing technology investments is to ensure the technology fits into a well-defined strategy—so that it can truly support the organization’s communication goals at the expected investment. This includes the extra time for implementation, configuration, ongoing administration, and maintenance of the systems/tools.

A well-thought-through communication strategy should define clear overall goals, priorities, and detail which audiences an organization plans to target. Once that’s defined, they can zero in on effective messaging, engaging calls to action, proper channels, and the best tactics to reach these audiences.

To manage the vast options from a technology perspective it can help to group tools, systems, and related processes in logical areas. Organizations can then invest in detailed planning for their prioritized areas based on the defined key strategies.

From our work we have found the following areas are logical groupings of tools and systems. Each area has its own vast array of best practices. This article focuses on only the key considerations for the operational and technical planning in each area.

Table of contents

  1. Content Planning, Production, and Management
  2. Product Information Management and eCommerce
  3. Cross Channel Customer / Audience Experience
  4. Customer Data Aggregation and Management
  5. Cross Channel Budgeting and Cost Tracking
  6. Campaign Management and Workflow Orchestration and Automation
  7. Analytics and Insights (Spend Optimization)
  8. Lead Generation and Management
  9. Advertising and Promotions
  10. Enterprise Technology and Data Integration
Content Planning, Production, and Management

Tools used here include social media management, CMS, design, photo or video editing suites, and digital asset management (DAM). 

Best Practices:

  • An efficient and well-defined process spanning the full content lifecycle should include:
    • Who sets and manages the budgets and priorities for content generation and/or procurement
    • Who produces and/or procures content
    • Who approves content 
    • How content is retouched, stored, shared for use by marketing, direct, and indirect sales channels
  • Folder structures will be needed to support the creative workflow. The key here is to avoid file duplication and to assess the need of a DAM system for sharing of content with teams outside of core creative. 
  • File naming and tagging conventions that ideally incorporate IPTC header fields to capture key content information.
  • Metadata management will become important for large content sets in a DAM that can include asset types, descriptors, author, owner, usage rights, and more.
  • Even medium-sized organizations are well-advised to hire a dedicated content manager or digital librarian 

Reference Link: 

Product Information Management and eCommerce

Tools used here include product information management (PIM), product lifecycle management (PLM), ERP, eCom platforms, and some overlap with digital asset management (DAM).

Best Practices:

  • A clearly defined data management strategy answers these questions:
    • Which systems are systems of record, and for what type of product data?
    • How will data flow between the different systems that hold product-related data—ideally minimizing manual re-entry?
    • Which team is responsible for data entry and accuracy at every step of the product lifecycle?
    • Who is responsible from a technical perspective for the maintenance of each system, the integrations, and importation & exportation of data from those systems?

For companies with a large set of product SKUs the product data management strategy will overlap with the overall enterprise data management strategy covered below.

  • If your product data is sent to external partners or vendors, planning for a flexible data export modeler is essential. Most PIM systems will provide some out-of-the-box solutions for the larger eCom channels like Amazon but you may need to provide data in many different formats if you feed multiple eCom channels. 
  • If you sell in or plan to expand into international markets the product data management system becomes significantly more complex and we highly recommend working with an experienced implementation partner.

Reference Link: 

Cross Channel Customer / Audience Experience

Tools used here include CMS and customer experience platforms (CXP) which often include some level of targeting, personalization, and testing. Audiences tend to engage with any brand or organization on multiple platforms and devices. Hence all social publishing, ad platforms, as well as traditional media platforms, are all technologies used to manage the user experience.

Best Practices:

  • Understanding your audience seems like such a simple concept but it remains one of the biggest challenges. Organizations tend to present experiences focused on their own view of themselves rather than spending the time and money to truly research what their audiences need, like, perceive as valuable, and find easy to navigate. If you want impactful experiences there is absolutely no way around doing that research and doing it repeatedly in our fast-changing world. The goal is to have well-defined and refined personas with strategies to engage each.
  • A well-defined, multi-channel brand style guide that truly reflects a design that’s very well aligned with your business is key.
  • Digital experiences spanning multiple channels need to be carefully planned so that the audience has a consistent experience and clear paths to action. Information architecture and navigation intuitive for your audience (not you) are relevant not just for your website(s) but should also span the full path or funnel from the first contact to action.
  • Volume does not equal engagement. Relevance based on persona and engagement stage as well as channel-specific content are critical and require detailed content planning. Content best practices vary by channel and it is advisable to get help from a digital agency. 
  • Beware of the complexity! To effectively reach defined audiences and personas, most digital media platforms allow for some level of dynamic targeting and personalization. Some even offer dynamic creative optimization (DOC)—changing creative representation and design on the fly. Be prepared for a steep learning curve as each platform has slightly different tools for segmentation and targeting. It will speed up the learning curve significantly if you get help from an experienced digital agency for the initial campaigns.
  • Tracking your customer across platforms and presenting the data in a way that uncovers patterns will be important to measure the effectiveness of your marketing efforts. More about this in the sections on Customer Data and Analytics below.
Customer Data Aggregation and Management

Tools used here include customer data platforms (CDP) or customer intelligence platforms (CIP), customer data management (CDM), and customer relationship management (CRM). In many cases, these tools include a level of artificial intelligence (AI) and self-learning or machine learning (ML) that can support segmentation, loyalty, and CRM.

Targeting and Personalization

Tools used here include targeting and personalization engines that are either stand-alone or part of a customer experience platform (CXP). Most use some level of AI.

Customer Loyalty, Relationships, and Lifetime Value

Tools used here include customer engagement software, advocate marketing software, and loyalty program software. In many cases these are also driven by varying levels of AI.

Best Practices:

  • In most midsize and large-scale organizations some level of customer data is stored in some form or another. Best practices in this area focus on leveraging that data for personalization to drive repeat business, upselling, loyalty, brand advocacy, and even product/market research. Privacy concerns will need to be addressed with customer data accessibility and opt-in versus opt-out options. Best practices in this area are also defined by the type of customer relationship: B2C, DTC, or B2B. 
  • Define a strategy that fits your relationship type. For example, automation for the masses versus decision support for high-value, service-oriented sales reps.
  • Clearly define which area(s) are the focus: drive repeat business, upsell, loyalty, brand advocacy, and product/market research. Each area has separate tactics and tools.
  • Always be transparent about the use of your customer’s data.
  • Consider the cost and complexity of managing large consumer data sets in a compliant way. Most organizations can gain tremendous value from their existing data without large technology investments by choosing and learning from marketing partners who have a strong data-driven practice and know how to experiment with more data-driven, test-and-learn marketing tactics.
  • Consolidating multi-channel consumer engagement/behavior into a massive consumer data platform (CDP) is only right for very mature organizations that have internal data experts on staff.

Reference Links:

Cross Channel Budgeting and Cost Tracking

Tools used here overlap with the area of campaign and marketing workflow and DAM. But workflow and DAM tools often lack true resource planning and budgeting. Martech areas specifically focused on resource planning and budgeting include marketing resource management (MRM), enterprise resource planning (ERP), financial planning tools, business intelligence dashboards (BI), and in many cases simple spreadsheets.

Best Practices:

  • Clearly identify the need around campaign planning and budgeting. Vendors from DAM to MRM and Workflow will offer confusing messages about resource management but many do not actually allow planning spend and tracking costs.
  • Managing campaign budgets for multiple channels like ad spend can in many cases be done in a spreadsheet by simply copying the information readily available in the social management tools. For larger organizations, BI tools like Google Data Studio or MSFT Power BI can automate data aggregation across multiple channels for a reasonable cost. More on BI tools in the analytics section below.
  • The cost associated with content generation and/or procurement can be harder to track than ad spend. Few tools focus on managing content cost and the return on content investment is even harder to specify. However, the content cost for larger campaigns or events can be significant and financial planning tools can certainly help manage the budget and track the cost. We recommend working with your CFO or finance team to leverage their expertise and toolset for the financial planning/tracking of larger campaigns and events.

Reference Links:

Campaign Management and Workflow Orchestration and Automation

Tools used here can be extremely confusing. Here are three classes of workflow tools out there:

  1. Many martech solutions offer some level of workflow orchestration and automation for tasks related to the function of that specific tool. For example, DAM tools allow for content request and production management. Social media tools allow you to automate the posting of content. Email automation tools (often synonymous with the confusing moniker Marketing Automation but meaning email only) allow for segmentation and scheduling of email campaigns. 
  2. The second tier of workflow automation solutions consists of a crop of campaign management tools that focus specifically on marketing’s cross-channel needs. These tools can help streamline and orchestrate a process spanning multiple teams and tools or systems. They are relatively easy to implement and configure, yet they can grow to a high level of sophistication. 
  3. The third class of workflow tools are true end-to-end workflow automation tools. They can not only track tasks that need human interaction, indicate task completion, and provide status updates but also interact directly with systems and tools. This is done mostly through APIs triggering automated tasks, processes within a system, and also passing data between systems. 

Best Practices:

  • Clearly identify the area and scope of the workflow. Are you seeking to manage a specific set of tasks? (See category 1 above) Or a wider process leveraging multiple teams and tools? (See category 2 and 3 above)
  • Workflow and Automation tools can:
    • Organize a process and alert people to tasks assigned to them
    • Schedule content for release to specific channels and audiences
    • Bring visibility to the status of a process or campaign via dashboards 
    • Allow for analysis of team performance 
  • Be clear about which area(s) you want to focus on.
    • In general workflow automation and orchestration via any tool only makes sense after a very clear process is defined and agreed to by all involved. Unusually 75% of the work is in defining and agreeing on the process in detail, including exceptions or fast track routes. Once the detailed process is defined with clear outcomes and responsibilities for each step, a workflow tool from the first or second category above can be configured with some experience in that area. However, deeper automation and data integration of the category 3 above will usually require a development resource and ideally deep domain expertise from an integrator. 

Reference Link:

Analytics and Insights (Spend Optimization)

Tools used here include site & social analytics, general BI tools, application-specific analytical capabilities across a wide set of marketing tech tools and systems, as well as artificial intelligence (AI) driven decision-making tools.

Best Practices:

  • As analytics is a vast area that can encompass anything from simple website visits and pageviews to highly sophisticated predictive customer behavior or market volatility—it is very important to define what specific insights you seek to gain from your analytics and what decision you hope to support with these insights. As far as the marketing and sales funnel are concerned, specific best practices and tools exist for each phase of the customer journey.
A graph describing the various stages of customer touchpoints:
- Awareness: PR. Radio/TV, Online ads
- Consideration: Social ads, E-mail, Reviews, Blog, Media, Direct mail
- Purchase: Website, Contact center, Store, Webshop
- Retention: Community, FAQ/Knowledge Base, Loyalty program, Newsletter, Blog
- Advocacy: Social media, Word-of-mouth

In general there are two sides to marketing analytics:

  • One side is to measure the effectiveness of the tactics used to influence the desired behavior in each phase of the journey.
    • Most tools that are used to engage the customer have their own data aggregation and analytical report and dashboards. It is important to truly leverage the insights of each tool to optimize reach and impact with the set spend. Volume does not equal impact and different audiences will react differently to different tactics. Hence, it is absolutely critical to fully leverage the insights each tool can provide. Investing in the education and training of teams around analytical insights is critical to elevate the analytical maturity of any organization.
    • However, most campaigns will leverage multiple tactics and channels. Hence, even organizations with smaller marketing budgets will soon find that aggregating data for insights, attribution, and decision making across social, site, blogs, podcasts, and more channels is a real challenge. While there are now tools like Google Data Studio and Microsoft Power BI that make aggregation and visualization of diverse marketing data easier, most organizations will benefit from expert advice in the configuration of these BI tools and the creation of reports.
  • The other side of marketing analytics is that of the customer. What are the most effective  marketing activities to drive the desired customer behavior for the specific customer segment? Please see the section above dedicated to customer data for this aspect of analytics.

Reference Link:

Lead Generation and Management

Lead generation tools usually focus on B2B environments and work as part of or are integrated with a CRM system. For consumer-focused companies, the section on Consumer Data above is more relevant. 

Lead generation tools will collect data from different channels such as email, web, or social and aggregate engagements across these channels into a lead score. This in turn can be fed into a CRM tool to give sales or business development teams a consolidated view of the customer’s engagement via marketing or direct channels over time. There is also overlap with marketing automation described above where certain lead scores can trigger automated follow-up engagement or alert sales teams to the activity of a specific customer.

Best Practices:

  • This strategy requires you to define your lead sources and thresholds. Which channels will be important lead sources for your organization and what level of activity qualifies as a lead? 
  • If you have a lot of engagement from potential customers or clients the amount of data collected can become overwhelming. Most organizations define different categories of leads (warm, cold, etc.) and configure the systems accordingly to focus your team’s attention on those leads most likely to generate revenue. Understanding where your leads will come from, how you will categorize leads, and handle the data before you choose your tool will be key to budget-conscious efficiency. 
  • Another big piece is understanding the integration with other tools, specifically CRM. Some CRM tools will have bundled lead generation and categorization capabilities. If you choose a separate CRM tool to manage your client relationships from the lead capture tools you need to make sure there is a solid integration strategy in place.

Reference Link:

Advertising and Promotions

Tools used here include those that allow you to place ads or paid media, set targets, and traffic ads to media outlets and print publications. There is significant overlap with social publishing tools that provide these capabilities for a variety of social channels. 

There are also increasingly sophisticated tools and services that focus on placing advertising on 3rd party sites and traditional media. Many digital advertising tools now allow for dynamic targeting, retargeting & personalization, test & learn, and on the fly dynamic creative optimization (DOC).

Modern ad platforms will also provide analytical data about the reach and impact of the ads that in turn can be fed into dashboards for deeper cross-channel data visualization. See the topic of analytics above.

Best Practices:

  • Ad campaigns can have many different objectives, targets, and aim to drive customer actions at different stages of the sales funnel. Given the overwhelming choices of advertising options, it makes sense to work with an experienced ad agency to plan larger campaigns and measure the impact.
  • The ad content and the calls to action they drive are only one side of the coin. Creative-focused agencies or in-house teams are a key ally in designing the campaign content for specific channels and ensuring that the customer journey is a clear, easy-to-navigate experience.
  • Media buying is the other side of the coin. Here specialized services are offered by agencies that focus on identifying the right 3rd party media based on your target audience. They place the ads and ensure the analytical data will be available for your team to review or feed into your internal BI tools (see more about BI tools above under analytics).
  • For larger campaigns that include owned and bought media, it’s common to work with multiple agencies and internal teams simultaneously. The coordination between the different creative and media teams can be handled by a lead agency, also referred to as agency of record (AOR), or by an internal strategic marketing team.
  • Many smaller organizations do not have strategic marketing skills in-house and in that case, even smaller campaigns can initially be supported by an AOR to ensure your marketing spend is well aligned with your campaign goals. They can also confirm that your creative and media mix is well thought through and executed in ways that strengthen your brand message rather than dilute it. Even smaller campaigns can yield a lot of relevant and important data about your customers and an experienced agency will support you in leveraging that data for decisions on refining your marketing activities.
Enterprise Technology and Data Integration

Tools used here include all the above and those that manage large data sets with a focus on sharing that data between many different systems. Master data management (MDM) is a category of tools that can make sense in this area.

In general, it can be said that there are a few differentiated data pools in most organizations that can be tackled based on the priorities of a given business model.

  1. Product data
  2. Consumer/Customer data
  3. Marketing analytical data
  4. Sales data
  5. Operational data
  6. Financial data

Since this article focuses on marketing technology only the first three data pools are covered in a dedicated section above.

Best Practices:

  • The key in developing an enterprise technology and data integration strategy is to not listen to vendors too much but seek an experienced independent enterprise architect and data strategist to help define the tenants of said strategy. Be aware of the Adobes, Microsofts, IBMs, and Oracles of the world who love to pitch their services in this area. An experienced independent architect will ask many questions and have work sessions with your team before diving into the details of specific technology and vendor selection.
  • While a comprehensive data integration strategy encompassing all the data pools mentioned above may make sense, it is also overwhelmingly complex. In most cases, it will make sense to ensure the business-critical data is managed and shared efficiently and securely between systems or visualized for effective decision support. The key is to make conscious decisions about what data pools to better integrate and do so against a well-defined data integration strategy that may or may not seek to be all-inclusive.

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