Introducing Sarah Halls, Customer Success Manager at Tenfold.com
This week’s blog post comes from Sarah Halls of Tenfold.com (the Customer Experience Cloud). Sarah Halls is the Customer Success Manager at Tenfold.com and her role involves understanding customers’ business goals, helping clients reach success, and partnering with Tenfold’s engineering team to develop solutions for end-users.
Note to Readers: This article is very thorough and features more than 2,100 words about the identification of data faults and how to transition to cleaner reporting practices. If you’re joining us from a mobile device, we recommend adding this article to your bookmarks and revisiting it later.
Does Your Sales Data Cause Inaccurate Forecasting?
By definition, forecasting is inexact. It is a data-focused assessment of possibilities; and yes, there is a lot of guesswork involved. The idea is to minimize the guesswork through the systems and strategies you implement – as well as the accuracy of the data you use.
If your sales data is causing inaccurate forecasting, then a review of your data input methodologies, sources, and management is necessary. Accurate data is the first stage to accurate forecasting – and there are a lot of benefits to forecasts that put you a step ahead of your competition.
Forecasting in Current Business
Forecasting, across the entirety of a business’ operational and strategic aspects, is necessary. Guy Rudolph, Vodaphone Director for Business Planning, even says that it’s “absolutely fundamental.”
In 2016, KPMG, an auditing and finance company, published a global survey with more than 540 senior executives as respondents. All respondents agree on the importance of accurate forecasting. 67.5% look to forecasting as a way to identify growth opportunities. 54% say that it guides them in the implementation of performance milestones, such as sales targets and quotas. Furthermore, 46% see forecasting as a means to improve organizational processes.
The problem lies in the accuracy of forecasting. According to the same group, unreliable forecasting can cost a business time and money spent on projects and initiatives. Even being as little as 13% off mark can impact share prices. On the flip side, respondents have seen share prices increase up to 46% with forecasting that’s within a 5% accuracy rate.
Bad Data = Inaccurate Forecasting
The same survey says that executives observe a lack in the quality of their sales data. A 2013 study by business services company Experian has found that 91% of businesses suffer from data errors. Compounded with the disuse of available external resources (only 40% of the KPMG respondents use reputable external resources), such as economic reports and the like, there are businesses that forecast with bad data.
However way you spin this, it’s bad for business.
In fact, the same Experian study has found bad data to be responsible for an average 12% loss in revenue. Another study, this time by DiscoverOrg, sees a loss of about 550 sales man hours or around $32,000 due to bad data.
Loss from bad data and inaccurate forecasting is not limited to money. Time and opportunity are also wasted.
Correcting Bad Data
The cost is high when it comes to bad data. As Brian Carroll of MECLABS puts it: “Garbage data in, garbage results out. Whether you do inbound or outbound marketing, the quality of your database and lists has a huge impact on your results.”
Once you observe compromised sales data, you need to take action.
What It Means When You Suffer From Bad Data
Inaccurate forecasting is just the tip of the iceberg. Forecasting is the basis of several key aspects in business operations. With bad data, the decisions you make for your business stand on shaky ground.
Incorrect business intelligence: Bad data skews the results of your analyses and forecasting, which unfortunately will be the bases of many executive decisions. It defeats the purpose of collating relevant information and analyzing them to help with your decision-making.
Flawed sales and marketing automation: Sales and marketing automation tools help you gather more data. For instance, automated follow-ups update the status of your leads, which then factors into your sales pipeline projections. With bad data, such as an incorrect email address, this can only lead to missed opportunities.
Lost opportunities and wasted time: Inaccurate data can lead to incorrect goal-setting. For instance, 77% of the Experian study respondents claim to have failed to reach their targets because of bad data. This can extend into the long run, in which underperformance and missed opportunities become norm.
Causes of Bad Data
There are several causes of bad sales data. Foremost of which is human error. As mentioned in Tenfold’s previous blog post, Trade-Offs in Good Data and Productivity, there is a prevailing notion that data accuracy, which entails administrative work such as data entry, cuts into a salesperson’s productivity.
Fostering this idea results in human negligence, at the very least. Team members might just breeze through the task, disregarding accuracy and completeness. Or, they might even ignore it completely.
This leads to another common cause of bad data: lack of communication and leadership. It is likely that sales teams and executives attach different weights to available data. While executives need it to forecast and make informed decisions, it represents more of a burden to salespersons.
It becomes important for management to step in and communicate the long term importance of accurate data. Good data leads to accurate forecasting; which, in turn, benefits a business and spurs its growth.
Fixing Bad Data
At the sign of bad data, take action to fix it. This entails more than just correcting the data itself. It involves implementing a top-down initiative that makes good data integral to your business operations.
Address sales team-generated errors internally: An effective way to address data negligence is to keep it internal to your organization and team. This considers differences in departmental procedures and tasks. You can communicate understanding for their predicament, as well as lead by example. At the same, this puts you in the best position with regards to the best practices and standards in data quality, as they apply to your department.
Data quality is top-down so lead by example: With data quality, consistent leadership is necessary. This means going beyond starting initiatives and then paying it lip service. The integrity of your data is a long term goal, which reaps long term benefits. So, start initiatives and be consistent with it. Lead by example.
Typically, there is a recognized leader when it comes to a company’s data quality; usually the Chief Information Officer (CIO). However lately middle management has taken the rein.
Have an organization-wide quality control process in place: An organization will have several data entry points, across its various departments. This makes an organization-wide data quality process even more important. Standards and best practices should be maintained regardless of entry point.
There are several tools available to help you cleanse data and maintain consistent quality. While 23% of businesses still implement manual data checking, it is best to automate the process. Use available software, such as UnDupe, to check for obsolete, duplicate and incomplete data.
Sales Data, Big Data and Forecasting
As of 2014, enterprise companies have invested a total of $8 million in big data. At the same time, 70% of these organizations have begun to implement big data initiatives.
Big data refers to the storage, processing and management of massive amounts of transactional and analytical data. This data is both structured and unstructured, and is made possible by advances in data storage technology. And yes, this would eventually include the sales data you sometimes neglect to input.
Big data is the ideal in making data-driven informed decisions. With more data, you can enjoy lesser margins of error. The condition here, of course, is that you’re working with good data. Bad data tips the advantages of massive data towards massive possibilities for errors.
Big Data and Forecasting
Integrating big data, alongside your sales data, with your forecasting and CRM system can help you operate more effectively, and reach out to a bigger market.
Implement benchmarks – Through accurate metrics and forecasting, you can implement achievable benchmarks that pit you alongside your industry’s best. This is a good way to push your team to achieve what’s possible. At the same time, you are able to measure yourself against competition.
Know your customers better – With big data and your CRM, you can know your customers better across different communication channels, such as your website, email, inbound calls and social media. This gives you a clearer view of your ideal customers and their behavior. You can forecast and anticipate their needs; and provide them with timely content, products and marketing campaigns.
Better data-driven decision-making – Good reliable data results in reliable forecasting, which is something you need when you want to make better decisions for your business. The more reliable data you have, the lesser your margin of error is. Your projections become more accurate, and you can act accordingly.
Predictive modeling – Big data improves the accuracy of your customer behavior forecasting. You are able to incorporate metrics, such as online engagements, demographics, and behavior histories; and come up with an accurate picture of your model customer behavior. With predictive modeling, you can anticipate needs and become more responsive.
Working towards Accurate Forecasting
Data quality monitoring is crucial, especially as the data available to your organization grows. Data cleansing and fixing are regular tasks that ensure you stay on this path. It is a way of building towards forecasting that improves in accuracy with time.
Accuracy should always be the goal. And, while there are naysayers who claim that accurate forecasting is impossible, there are steps you can take that move you closer to forecasts you can rely.
The initial step is to develop a mathematical model that is predictive of your customers and industry. It should include these six crucial metrics:
- Sales revenues and profits during previous years
- Buying trends, based on season and your customers’ buying patterns
- Your previous marketing campaigns and their historical impact over a given period of time
- Market and industry health/ state
- Economic health/ state
- Currency value fluctuations
There are other important metrics you might consider including in your assessment. It really depends on your market and industry. Tweak the factors you consider as you go along. Forecasting can be considered a practice in getting to your target audience and industry better.
It will not only help you in improving your decision-making through the use of forecasts. You can also potentially vary your product development and marketing campaigns, based on the industry trends that you observe.
Seeing Forecasting for What It Is
A big flaw in how many currently approach forecasting is that it is now burdened with expectation. From data-based projections, forecasting has become a given. If we don’t reach our forecasted goals, there must be something wrong with the team. As business consultant and growth leadership expert Scott Edinger echoes: “Those projections immediately become promises, whether it’s to the sales manager or to Wall Street.
So, instead of allowing accurate forecasts to guide the way towards better decision-making and smoother business operations, it has worked more like a double-edged sword. Sales goals hang over the heads of hapless sales men and women, even as certain external and internal conditions change.
A better approach to forecasting is to view it as a way towards achieving an improved understanding of your business, industry and customers. This way, even as external and internal factors change, you retain a good grasp of the market. You become better equipped to respond to these changing factors, and remain afloat, moving towards success.
Edinger advises: “If you use your forecast that way, then it can become a useful tool that you can look at on a near-daily basis to understand where you should focus your efforts and your energy, where you need to follow-up and where you need additional resources . . . instead of being an exercise where I’m going to get raked over the coals by my manager because something changed.”
As we mentioned earlier, forecasting – even accurate forecasting– is inexact. It’s subject to changes, some of which we can’t control. Thus, it shouldn’t be approached with rigidity.
Instead, see forecasting for what it is: a view into possibilities. To develop business systems, processes and strategies that are based on good data and reliable assessments. This is how your business can be responsive to the market you service and the industry you compete in.
Subscribe to our Blog
Sign up to get notifications of new blog posts we released on our website!