Mary looked at the calendar as she started her workday. It was already the second week of January, signaling that she and her team only had ten weeks to go until the end of their fiscal year.\u00a0 It was planning time.\u00a0\u00a0<\/p>\n
What could she do with her team in the next fiscal that would help her company overcome the slump in revenues they had experienced in the previous three quarters?\u00a0 It had to be different.\u00a0 She couldn\u2019t keep spending money on search words, emails, and sales events that didn\u2019t produce revenue. The content for the new product line needed revamping, and sales were carping about the lack of leads. She also knew her management wanted to reduce her staff by 20%.\u00a0 That would mean taking her team down to the bare bones.\u00a0\u00a0<\/p>\n
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What could she do with the tools she had and the AI capabilities that were cropping up in new versions of those tools? What kind of team did she need to ensure that AI was managed effectively, efficiently, and ethically?\u00a0\u00a0<\/p>\n
Prompt: What are the challenges for marketing ops in 2024?<\/h2>\n
She started by looking at what her favorite generative AI tool said about the challenges she and other marketing managers faced.\u00a0 She entered the query: \u201cWhat are the challenges for marketing operations in 2024?\u00a0 Out came a long list.\u00a0 On that list were two challenges that she was very familiar with \u2014 data quality and organizational silos.\u00a0 Those, coupled with AI, formed a powerful triad.\u00a0\u00a0<\/p>\n
These three challenges complicated her options for building, developing, and managing her team. Together with AI advances and a marketing landscape of rapidly changing technology and consumer buying behaviors, they produced a \u201cperfect storm\u201d that could destroy the best of her efforts, even with the most competent support.\u00a0<\/p>\n
Mary began to tackle this new marketing landscape with the end in mind.\u00a0 Her goal for the year was to execute efficiently with a smaller team while generating the MROI she knew the business needed. First, she needed to ensure that her team had the skills and attributes required for this task.<\/p>\n
Look for your Tweeners<\/h2>\n
Having recently attended a conference where she heard data guru Tom Redman<\/a> talk about \u201cPeople and Data,\u201d his latest book, she turned to its pages for inspiration. Tom pointed out that this new marketing environment requires a new kind of person. \u00a0 Tom had called the new person a \u201cTweener,\u201d someone who can make sense of one area\u2019s outputs so that those outputs can be understood and used as inputs for another area.\u00a0 Marketing and sales were two good examples.\u00a0 She already knew she wasn\u2019t doing an excellent job making that connection.<\/p>\n Redman\u2019s words resonated with her: \u201cThe problems we have today are not scientific.\u00a0 As far as skills go,\u201d he said, \u201cwe need more poets than AI experts.\u201d\u00a0 He went on to explain that poets have a broader view of concepts.\u00a0 \u201cThey look at things in many dimensions.\u201d\u00a0 This ability to absorb the bigger picture and make connections is best done by someone Redman called a \u201cTweener.\u201d\u00a0 Although he sees this person as a connector of business functions and datasets,\u00a0 this \u201cconnector\u201d role is also essential for the new marketing operations environment.\u00a0<\/p>\n But where does Mary start?\u00a0\u00a0<\/p>\n Mary created a list of skills needed to execute next year\u2019s strategy.\u00a0 Here\u2019s a list of those skills in priority order:<\/p>\n Mary knows that should she reduce her staff. She will face choices about what skills are essential to her marketing efforts.\u00a0 Some of the skills she may find missing can be bought from outside agencies, but which one should she buy versus grow inside her organization?\u00a0 She lists the pros and cons of maintaining an inside team versus outsourcing each skill required.\u00a0 She does not consider costs but knows that buying some capabilities can be expensive.<\/p>\n Mary evaluates the skills she needs on staff versus those she can buy from outside the organization or transfer to other internal organizations.\u00a0 For example, it could be beneficial to hire an outside agency to do the first follow-up on all inquiries received from online marketing efforts or bring in a company that can quickly apply AI to the marketing data set to improve its quality \u2014 dedupe, correct names, email addresses, etc.\u00a0 And what if she could convince finance\u2019s group of analysts to provide her with marketing reports that are only slightly different from those provided to department managers, reports that show what she is spending and where?\u00a0 All these actions would help her.<\/p>\n Here\u2019s her analysis of whether she should keep skills in-house.<\/p>\n With this list of skills, Mary assesses her team\u2019s attributes. She recognizes that skills are learned and can be taught.\u00a0 They are capabilities that are applied to tasks daily. \u00a0Attributes, however, are inherent personal qualities that can\u2019t be taught. They make up one\u2019s personality. Although attributes are easy to name, Mary has some issues evaluating them in her team, so she takes each attribute and identifies behaviors she would like to see for each:<\/p>\n With skills and attributes identified, Mary considers how to manage this new Tweener role.\u00a0 Was it a skill or an attribute, or both?\u00a0 Where exactly did the role belong?\u00a0 In one dedicated person or within a skillset that many jobs had?\u00a0 Could she hire a specialist in one area (Customer Experience, for example) but have them collaborate with other areas, such as SEM or Events?\u00a0 Would the work product be as effective or efficient?\u00a0<\/p>\n These questions led Mary to her next step.<\/p>\n The most obvious place to put a Tweener is between sales and marketing, but seeking out a place within marketing to start the experiment seemed more reasonable to Mary.\u00a0 She was looking for someone on the team who could grease the gears and glue together functions, a place to put a Tweener, but within her control.<\/p>\n Mary went back to the three challenges for marketing operations:\u00a0 data, organizational silos, and AI.\u00a0 How could she use AI to help her improve her data and dissolve the walls within her organization?\u00a0\u00a0<\/p>\n Mary knew that AI could and would probably take over many marketing operations.\u00a0 There were already bots to manage data movements between applications and tools.\u00a0 Her marketing team could be more efficient now that many of their marketing tools had built-in language creation and editing capabilities. \u00a0 With AI-developed scripts for telemarketers, leads could be generated faster and more efficiently.\u00a0<\/p>\n Tools with built-in AI could also help manage data quality.\u00a0 Informatica, Ataccama, Data Ladder, Informatica, and Talend already use AI to solve data quality issues.\u00a0 With the improved data quality of her customer data, Mary would be more confident in using that data to build AI models for the company.\u00a0 Those models could help all departments answer their most perplexing questions.<\/p>\n Suddenly, Mary had an idea:\u00a0 What if she created a Tweener role that could help her various marketing operations areas use AI to improve their processes, data, and even their relationships? Her Tweener role would be someone who knew AI but could also help others apply it.\u00a0 She would look for someone with Tweener attributes as well as AI skills.\u00a0 She would assign the Tweener to her lead generation team and have a plan developed that generates a 100% increase in leads for the new year.\u00a0 She decided that the Tweener would work with lead generation and the inside sales teams to ensure that AI contributes to the processes in the right places.\u00a0<\/p>\n Dig deeper: What is marketing operations and who are MOps professionals?<\/a><\/em><\/strong><\/p>\n Mary was excited about her possibilities for the new fiscal year.\u00a0 Taking the time to think through what areas of the business were most important to her operations team helped her better understand where the team had gaps.\u00a0 She could use this knowledge to set a training curriculum as well.\u00a0 Since Mary picked a challenge for the team involving AI, team members would also increase their understanding of this technology and what the company gains by applying it.\u00a0 She was looking forward to understanding better how the role of a Tweener could help her bring organizations together and erode silos.\u00a0\u00a0<\/p>\n Mary also understood that these changes \u2014 adding a Tweener role and tackling lead generation with AI \u2014 would create challenges for the team. Change was always hard, and having an open mind about new ideas was imperative. George Bernard Shaw\u2019s quote rang in her ears: \u201cThose who cannot change their minds cannot change anything.\u201d\u00a0\u00a0<\/p>\n She knew it would be her challenge to bring change to her organization. Still, if they successfully reached their goal, they would elevate marketing capabilities within the company and begin the process of eroding the silos that had been created.\u00a0 As she closed the day, she thought of what Tom Redman had said about the new roles the business needed.\u00a0 \u201cWe need more poets,\u201d those who can think broadly about the topics that will change our world.<\/p>\n From AI to layoffs, it\u2019s been quite a year. How has it been for you? Please take our short survey so we can tell you the state of martech salaries and careers.<\/p>\n<\/div>\n<\/div>\n <\/p>\n Get MarTech! Daily. Free. In your inbox.<\/p>\n<\/p><\/div>\n<\/p><\/div>\n<\/div>\n \n<\/div>\n Opinions expressed in this article are those of the guest author and not necessarily MarTech. Staff authors are listed here<\/a>.<\/em><\/p>\nEvaluate skills needed\u00a0<\/strong><\/h3>\n
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Staff or buy the skills<\/h3>\n
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\n Skill<\/strong><\/td>\n Pros<\/strong><\/td>\n Cons<\/strong><\/td>\n<\/tr>\n \n Data analysis<\/strong><\/td>\n Marketing data is clean and connected to sales from a company\/customer perspective.Data understanding is complete across the organization; data governance addresses privacy considerations and helps control data usage. Data science and AI skills exist in 10%+ of the marketing organization. Marketing values data as an asset<\/td>\n Marketing data is not as complete as teams need; data is missing some components necessary for selling new products. Understanding of the data process is spotty across marketing teams. Data science and AI skills are dedicated to specific sub-teams and often don\u2019t share data or outputs of data models. Enterprise wants to deploy AI algorithms but doesn\u2019t completely understand the ramifications of AI deployments<\/td>\n<\/tr>\n \n Digital Marketing Knowledge<\/strong><\/td>\n The team understands the customer journey and how digital enhances the customer experience. SEM and SEO experts have in-depth product knowledge and are dedicated to specific product lines.\u00a0The team is adept at managing technical contacts and conversations on social media.\u00a0<\/td>\n The team has limited experience with measuring customer experience. SEM and SEO experts do not apply their expertise to the overall company presence on the web.\u00a0The team does not always know when to send contacts and inquiries to sales.\u00a0<\/td>\n<\/tr>\n \n Sales and Marketing Automation<\/strong><\/td>\n The organization has five tools managed by internal experts in each. The team uses machine learning to identify customers with the propensity to buy.<\/td>\n Lead handoffs to sales are messy because the sales teams are constantly changing. The team has not experimented with AI beyond propensity models and could be looking at AI for improving data quality, AI for predicting or forecasting.<\/td>\n<\/tr>\n \n Content Creation and Management:<\/strong>\u00a0<\/td>\n Generative AI bolsters content creation and management.<\/td>\n Lack of product knowledge keeps content basic.<\/td>\n<\/tr>\n \n Project Management:<\/strong>\u00a0\u00a0<\/td>\n The team has three certified project managers who lead large projects and campaigns.<\/td>\n The project managers have not been trained to apply AI to planning and scheduling.<\/td>\n<\/tr>\n \n Measurement management<\/strong><\/td>\n The team has developed a complete package of measurements that is distributed regularly.The measurement package measures MROI, lead conversations, and other basics. \u00a0<\/strong><\/td>\n The team does not help managers understand the numbers.\u00a0The team does not have good data for measuring customer experience.\u00a0<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n Evaluate the attributes of the team<\/h3>\n
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\n Attribute<\/strong><\/td>\n Descriptions<\/strong><\/td>\n Recognizable Behaviors<\/strong><\/td>\n<\/tr>\n \n Open-minded<\/strong>. \u00a0 <\/td>\n A person willing to consider new ideas, perspectives, and experiences without prejudice or bias.\u00a0<\/td>\n Curious \u2014 ask questions.
Empathetic \u2014 feel others\u2019 pain.
Non-judgmental.<\/td>\n<\/tr>\n\n Rapid learner<\/strong>\u00a0<\/td>\n Someone who can learn new things rapidly from reading, listening, observing, experimenting, and doing.\u00a0\u00a0<\/td>\n Learns new technologies rapidly. Reads widely and often<\/td>\n<\/tr>\n \n Good listener and question-asker:\u00a0<\/strong><\/td>\n One of the most time-honored ways to learn is to ask questions of those who know something you don\u2019t.\u00a0This attribute combines asking a good question with listening carefully to the answer.<\/td>\n Actively listens.\u00a0Asks questions for clarification.\u00a0Questions demonstrate an understanding of the discussion topic.<\/td>\n<\/tr>\n \n Simplifier\u00a0<\/strong><\/td>\n Someone who takes a complex subject, breaks it into bite-sized pieces, and communicates an understandable narrative.\u00a0<\/td>\n Often clarifies others\u2019 comments in meetings and offers explanations.<\/td>\n<\/tr>\n \n Precise communicator<\/strong><\/td>\n Someone who uses data and facts to communicate clearly and objectively.<\/td>\n Presents facts and data about a situation first before making recommendations.<\/td>\n<\/tr>\n \n Even-tempered Juggler<\/strong><\/td>\n A person who never lets you see them frustrated keeps a cool head and listens for the compromise position. Combines an even temper with the ability to juggle several projects, programs, or things simultaneously, and everyone benefits.<\/td>\n Often has a crowded schedule and an extensive to-do list but seldom complains.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n Determine where Tweeners or connectors can best serve the team<\/h3>\n
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What results to expect<\/h2>\n
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<\/figure>\nTake the MarTech Salary and Career Survey <\/h3>\n
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