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15 Reasons Why Analytics Prediction Will Make You a Better Marketer

looking by katieb50 15 Reasons Why Analytics Prediction Will Make You a Better Marketer

badge guest post FLATTER 15 Reasons Why Analytics Prediction Will Make You a Better MarketerThanks to the success of Google Analytics, there are dozens of ways to collect historical data from your website and connecting social networks. And yet, your optimization strategy is probably missing one very important step: predictive analytics.

Predictive analytics interest has skyrocketed over the last year, according to Google trends. The reason? Marketers are beginning to understand how forecast models play into strategy testing and optimization.

The next wave of calculated business improvement has leveraged its success on analytics forecasting and simulation accuracy. Since predictive analytics combined with marketing data is still relatively new, there are only about a dozen ways to forecast your future based on historical data – ranging from long-tail DIY to automated software.

Regardless of your software choice, each forecasting process works approximately the same:

It takes your historical data and runs it through a series of algorithms and constraints, which consider weighted averages and seasonality. Once you have developed a forecast, you are presented with a customized projection of your future marketing data.

If you still need examples of how predictive analytics can improve your site’s testing and optimization, here are 17 reasons why your team needs to implement predictive analytics right now.

1. Learn How To Achieve Your Goals This Year

With simulation capabilities, you can plug your actual goals into your future and see what other corresponding metrics need to follow suit.

For example, your goal might be 100 transactions from Facebook CPC traffic. Simply test “100” as your metric for CPC transactions and see how it affects other factors. You might find out that in order to get 100 transactions, your ad clickthrough rate needs to be at least 4%, your budget needs to go up $20/day, and you need to bid higher on average for your keywords.

2. Find Waste. Delete It.

In a forecasting model, you can see if your current strategy is broken. For example, after a few simulation changes you see that there’s no way you’re going to get traffic from a particular medium you’ve been betting on (i.e. organic, referral, or social). Forecasting can unveil some of your biggest pain points. If you can’t fix the pain points, get rid of them.

3. Discover Where You Can Leverage Efforts

When you’re looking at your marketing strategy as a whole, it can be hard to see the trees through the forest. What one metric makes the most difference? If you were to change one goal only, which would it be?

Use forecast and simulation techniques on your KPIs and find out which metrics and dimensions create the largest, most profitable change.

4. Find Your KPI Sweet Spot

Budget isn’t the only leverage point here, but we’ll use it as an example because it’s a universal one.

When you set a budget – marketing related or not – you usually try to come in under budget. Likewise, when one of your clients sets a budget, he or she usually intends to not only get great results out of the budget but also hopes you come in under budget.

Simulation tools let you test budget allocation (among other metrics) across your marketing ecosphere to find that strategy “sweet spot” where the highest yield births with the least amount of labor.

5. Make Decisions Faster

Use simulations alongside multivariate and A/B split testing strategies to make decisions faster, smarter, and more accurately.

6. See Trends Clearer

Graphs of your historical data are cool, but looking into the future is way more valuable. Why wait until next week to find out you’re wasting money? Use forecasting to see where your current strategies are taking you, then simulate changes to make the future better.

7. Save Money

Instead of just throwing money at campaigns that are working, test budget, ad cost, keyword bids, or other increases until you find the right balance (see #14 for an example). Additionally, change those downward trending strategies right now, before you’ve lost $1,000 to the keyword “bunny slippers.”

8. Save Time

If you have been forecasting in Excel (like we were), you can now breathe a sigh of release. Forecasting models are doing the work for you automatically. If you haven’t been forecasting at all yet… well, then just take our word for it; manual forecasting is a time suck.

9. Endure Less “Oops” Embarrassments

With forecasts, you can see where you’re going before you get there. So if your campaign is doing poorly, your forecast will show the campaign trending downward. And if you’re doing great, your forecast will show the campaign trending upward. Either way, you’ll be on top of your strategy and you’ll have less “Oops, I didn’t think it would do that” moments.

10. Be Smarter and More Confident

Imagine this presentation: You stand up in front of your client and say, “Based on your historical data, we can double your ROI by simply increasing web traffic by 1,000 a month. We can do this a couple different ways, but the most cost and labor effective strategy is to increase overall ad CTR by 2%, add an average of 20 cents to Adwords CPC bids and move our transaction rate from 1.3% to 2.5% by utilizing the landing page we recently optimized.”

This one is easy. You win by sheer numbers, and everyone loves to get behind those. No more relying on the level of creative minds in the room (sorry, Don Draper).

11. Do Less BSing

How many times have you just skipped over a metric during a campaign summary or update? You’re not lying by ignoring the metric in your summary, you’re just not giving the client so much information their head will hurt, right? Give your ethics a break with forecasting model outlooks, justifying any “bad” metrics with the projection of better future results.

12. No More Penny-Slot Testing

Don’t just put a few dollars towards a new campaign and see how it fares. Instead, put a whole budget towards it and know what’s going to happen next. Forecasting and simulations make “testing” new strategies less like testing and more like doing.

13. Better Team Planning

When you’re testing and optimizing, it’s easy to get your KPIs mixed up, especially during multivariate testing. Who’s in charge of what? Which variable did we decide is most important overall? When you’ve forecasted and simulated a plan, the path is clear as glass. Map out your goals per day, week, month, quarter, whatever you want, and start checking off those progress boxes.

14. Measure Optimization More Accurately

Optimizing and forecasting have a great relationship. The process works in almost a perfect circle. You setup an A/B or multivariate test, simulate the test results on the forecast model, find new opportunities within the forecast model, change your tests to focus on these new opportunities, and repeat.

15. Make Your Future Better

This is the most important point because it makes the most sense. Forecasting gives you the ability to see the future of your marketing data by testing changes on your marketing data and then making the next set of data better. Enough said.

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looking by katieb50 15 Reasons Why Analytics Prediction Will Make You a Better Marketer
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15 Reasons Why Analytics Prediction Will Make Your Life Easier
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Thanks to the success of Google Analytics, there are dozens of ways to collect historical data from your website and connecting social networks. And yet, your optimization strategy is probably missing one very important step: predictive analytics. Learn how predictive analytics can make you a better marketer.
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  • CJ

    Great article. Is there any software that you would suggest to use or any place where I could find some sales forecast formulas?

    • http://chrisconrey.com/ Chris Conrey

      CJ – That is one of the main uses of Levers, to do the forecasting via software instead of having to manually build up the models yourself. Feel free to check it out at http://leve.rs

  • doodleblue

    Deep analyzing for predictive analytics. Great Article.