Target audience: Marketers, analysts, campaign managers, and decision makers.
Preface: I teach multiple tools under Adobe's experience cloud and I often get to have a look at the shape of digital marketing in multiple companies and across various business domains. This post is a summary of the most common problems and ways of resolving them at early stages before they become blunders.
1. The accuracy (and single view) of data: If you work in a medium or big size organisation or own one, you would know about the multiple platforms and tools required, they can range from Data collection systems or CRMs to campaigning and social listening tools. With multiple platforms comes the problems of integration and thus making sure that the quality of data is intact is a major challenge. A lot of 'orgs' only look at one face of data while ignoring the other aspects of it. For an example, if you're an e-com company and you're worried about cart abandonment, add your partners and offline data to create a 360* data view and maybe you'll find out while customers look at the products online they tend to buy offline (or vice versa). By using a cross -channel data source I've seen a lot of companies saving big bucks on their marketing and especially retargeting.
Best practice: Have a 'quality check' team and use a good DMP. Get a data guru to look at the complete picture of data in your org.
2. People moving around (or one-man show): This is one of the most common problems, here's what happens: Person X (Data/marketing manager) takes the initiative of setting up a platform, onboards an agency, figures out the need and gap, makes a pitch and begins the implementation; usually by the time the platform is set, quality check is done and the tools are in place, it's already two years and this person moves ahead in career and to a different org thinking of the excellent foundation stone that has been laid. Fast forward six months; The new person in place is adept and comfortable to the platform which was used in his/her previous organisation, a recommendation is made for it and the system which got implemented is said to be a wrong choice. The whole system restarts into the same loop costing the company a fortune.
Best practice: A decision team (can also be one person each from relative verticals) makes a decision based on proper market research and everything included the pitch and benefits are recorded and logged, this gives an answer to why was a decision made in a particular manner.
3. Lack of training: Learning is a journey and most of the companies forget this fact, let's face it; employees are like cattle, they're invested in, are fed and are cared for in a way that enables them to produce as soon as they can, sometimes one can produce even though before they're ready, hence most of the times, the product will be shit. Learning also needs to be of quality, a lot of times going for cheap training programs instead of the standard will do more bad than good.
Best practice: Enroll in a learning program that spans quarterly (or every six months depending on industry), gives your employee certification and assessment goals.
4: Not having a precise KBR: A lot of times during the introduction I ask the participants what is it you want to measure and I see blank faces very often. In an opposite scenario, sometimes people will enthusiastically say 'everything', which is equally bad. Knowing what is the Key Business Requirement of your company is the starting step and around that, you can measure 'everything' that impacts it. Mostly KBR's are of three types: Engagement, Conversion & Revenue. I've also seen a few orgs which not only map their goals well but also refine metrics over a period of time to measure their success by best means.
Best Practice: Map the contributing factors of your success in your online and offline journey, define and convey the metrics that measure your success. Also get stakeholders in one room during the year and brainstorm that whether your success metrics are still relevant and if not what can be the ways of improving them
5. Big data not actioned upon is bad data: Businesses often generate tons of data but not all data is information, analysts, and marketing folks generate dashboards and schedule them and then over time keep getting the same reports without ever logging back in again. Agencies copy paste the same reports and metrics for similar businesses promising them that they're uniquely built for them. People get comfortable and stop questioning their data. I recently taught a class to the students of a university and I was amazed at the quality of questions thrown at me, most of the adults with 'X' years of experience don't do that, in fact, they hardly ever question their data in the right way.
Best practice: Get interns, they're a cheap way of getting new eyes to look at your business, it helps them and it helps you. Run A/B tests on your website to see what works and experience targeting to continue the user journey where they left. All the data will only make sense if you ask the right questions and that'll only happen if you've set the right KBR.
Postface: In an ideal scenario, companies should invest in their data planning, strategy, and execution in early stages, sometimes budget is a constraint but most often not implementing a good solution at an early stage blocks your view of your audience maturity. Plan your marketing around the personalized journey and use cross-channel data segments, partner with good data providers and discover multiple dimensions of your existing audience. Acquisition and Retention still are and will be the two major aspects of your customer but there is always a scope of refining the metrics you use to measure the two.
Linkedin Profile: https://www.linkedin.com/in/abhisheksrivastava5/