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Guidance and results for the data-rich, yet information-poor.

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Predictive Analytics Linked To Increases In B2B Profits

Predictive Analytics Linked To Increases In B2B ProfitsWhile many published examples of successful Predictive Analytics projects come from B2C brands, the B2B industry can also greatly benefit from these techniques. Many B2B companies have closed more leads and increased sales by a substantial amount after adopting predictive analytics.

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The Future of Marketing Automation Is In Predictive Modeling

Marketing Automation: Predictive Analytics

Marketing Automation: Predictive Analytics

Recent studies have shown that many businesses that buy into marketing automation experience buyer’s remorse. When around 70% of marketers are unhappy with their solutions and only 7% are seeing measurable ROI, it’s easy to dismiss the concept of marketing automation altogether. Despite the significant potential advantages and upsides that it can bring, the fact is that buyers are not getting the results they want.

Why are so many who believed in marketing automation so dissatisfied with the results they’re receiving? Simply put, there is a severe lack of automation when it comes to marketing automation. Email blasts consist of layers upon layers of forms, triggers, and other bloated processes that try to do too much but deliver too little. The workflows are extremely complex and hard-coded rules bound to precise user decisions mean you lose adaptability. It could take hours for a marketing automation system to process the workflow – causing you to miss a valuable lead.

Marketing automation was supposed to be fast, simple and automated. But again, this is another promise that wasn’t delivered by the vendors and platforms of today.

The future is in predictive modeling. Platforms of tomorrow will be more intuitive, sleeker and intelligent. These same platforms will be able to mine a treasure trove of information in order to make relevant suggestions to your customers.

The technology that’s currently available has evolved in leaps and bounds, with significant advances in the field of practical machine learning. Think about how accurate Amazon and Netflix suggestions can be. This is predictive analytics at work. Any company, no matter what size, can benefit from personalized modeling that’s scaled just for them due to more cost-efficient computing infrastructure. The marketing automation problems of old, like campaign optimization and lead nurturing, are now being addressed with the help of more effective and efficient solutions.

If predictive analytics and modeling used to be inaccessible, new platforms are quickly changing this and combined with specialized apps, it will become actionable and intuitive to use. Another main advantage it has over the clunky traditional use of marketing automation is that it will automatically change and adapt as your business grows. You’re looking at minimal configuration of tedious workflows and very little manual re-tuning. Predictive systems are able to learn from your business and customers, adapt from the results and then improve its efforts moving forward.

Exactly like how cloud computing has disrupted the software industry, we’ll be seeing predictive platforms change the way marketing automation is as a whole. In the near future, there will be more to the field of marketing than just tracking customer behavior and managing marketing campaigns.

Get ready for a revolution in marketing automation; predictive modeling is changing the game.

What’s Your Modeling Level?

Layered Pyramid Three LevelsHow long before we actually see some results here?

It’s a question the TMA team gets during a great many Q&A sessions. Everyone wants a time frame–some idea of the amount of time it takes to start getting actionable intelligence out of any given model.

But much depends upon the level of modeling that you are trying to engage in. There are three levels that you can choose from.

The first is the single model level. At this level, you’ll be building a single model for a single purpose. When starting from scratch, you can generally achieve this level within six weeks or so. However, in truth the single model level is not likely to produce much ROI. That’s not to say it’s useless–this sort of “level 1 model” makes an excellent pilot project, which can be used to demonstrate the benefits of predictive modeling to an otherwise recalcitrant leadership team.

The next is to build a repeatable process at the departmental level. This would be a model that’s run and updated on a regular basis. For example, your marketing department might regularly test the response to different advertisements in different zip codes to find out where it’s best to spend money and effort. They might re-run the model every six months to account for changes in external circumstances and demographics, and to adjust as the model prompts them to adjust. You can build a model like this in about six weeks, and you’ll get some solid use out of it.

The third and final level takes the most time–usually about six months. This is the “practice” level. That is, you’re going to create an analytics practice, a full department that serves the entire enterprise, a department capable of tackling a variety of business problems. As you can imagine, this is the analytics level that produces the greatest amount of ROI over the long run, even if you have to wait longer to see results.

One of the ways that TMA assists clients is by helping them build a true modeling practice, mentoring them so that the practice will ultimately be successful in the long term. It’s a great way to ensure that you maximize the actionable, profitable insights locked up inside of your data.

Remember, any grad student can use modern software to build a model in two weeks. Unfortunately, two weeks isn’t enough time to lay groundwork, provide context, or derive value.

It’s really only enough time to build a really fast rocket with no mission plan.

How Big Data is Like Teen Sex

EverybodyDoingItAn inflammatory headline? Maybe, maybe not.

Think about it.

Everybody talks about big data.

Nobody really knows how to do it.

Everybody thinks everyone else is doing it.

So, everybody claims they are doing it!

In reality, less than 20% of organizations have begun to develop data projects. Those who have completed these projects successfully are in the minority. Even then, many of these organizations are unsure whether to treat that successful project as a spot effort, or whether to make it an enterprisewide concern.

That’s a shame, because data projects do offer significant value. For example, a recent study called How Does Data-Driven Decisionmaking Affect Firm Performance? indicates that, all other factors being equal, organizations who adopt data-driven decision making see 5-6% more output and productivity than organizations who do not adopt data driven decision making.

The disconnect occurs precisely because companies get into a “big data mindset” instead of adopting a mindset which focuses on deriving actionable insight about pressing business problems through predictive analytics.

In truth, “big data” is just an amorphous term. One TMA trainer defines it as, “one byte more than I can efficiently manage in the time allotted.” Thus, what looks like big data today is, in fact, going to look like small data tomorrow. Big data isn’t revolutionary–it’s evolutionary. The business world has the opportunity to manage data more successfully by choosing to realize that.

Keep an eye out for TMA’s newest seminar, “Big Data, Rhetoric and Reality,” if you are interested in exploring these issues a bit further. It’s coming soon, and it will help you navigate your way through a mess of hype so that you can truly extract real value from your organization’s data.

Finding Good Analytics Books for Beginners

Composition with hardcover books in the library“Can you recommend some good books?” It’s a question that comes up quite often in TMA webinars.

Finding good books can be pretty difficult. That is because most of the books that are out there right now will actually lead you in the wrong direction.

This is because many books are still focused where many technicians are focused: on building better algorithms. They completely ignore the strategic approach that TMA teaches.

There is one good book. Competing on Analytics: The New Science of Winning” by Tom Davenport is a book which really focuses on getting the business value out of the analytics.

Of course, after you’re done reading Tom’s excellent work you might well want to dive in even deeper. At that point, it’s difficult to steer you in the direction of any good reading material.

That doesn’t mean you have to end your explorations, however. You can, for example, choose to download some open source software packages. Then, take a look at the documentation. Look at the example data sets.

This will give you a feel for how this type of analytics works within the context of setting clear, concise business objectives.

Of course, you can also choose to expand your education by signing up for TMA’s training courses as well. You’ll receive hours of materials which will help you dive into analytics in a way that will continue to produce results.

This is where you want to focus your energy anyway. You can build the most beautiful model in the world, but it’s not going to help you if it measures all of the wrong things. And reading all of the books in the world won’t help you until you wrap your head around a process which will help you derive actionable intelligence from the mountains of data that your organization has collected.