{"id":1452,"date":"2019-02-18T00:24:20","date_gmt":"2019-02-18T00:24:20","guid":{"rendered":"http:\/\/kusuaks7\/?p=1057"},"modified":"2023-07-14T10:19:31","modified_gmt":"2023-07-14T10:19:31","slug":"monetizing-data-4-datasets-you-need-for-more-reliable-forecasting","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/bigdata-cloud\/monetizing-data-4-datasets-you-need-for-more-reliable-forecasting\/","title":{"rendered":"Monetizing Data: 4 Datasets You Need for More Reliable Forecasting"},"content":{"rendered":"

In the era of big data, the focus has long been on data collection and organization. But despite having access to more data than ever before, companies today are\u00a0reporting<\/a>\u00a0a low return on their investment in analytics. Something\u2019s not working. Today, business leaders are caught up in concerns that they don\u2019t have enough data, it\u2019s not accessible or it simply isn\u2019t good enough. Instead of focusing on making data sources bigger or better, companies should be thinking about how they can get more out of the data they already have.<\/p>\n

Contrary to popular belief, a high volume of perfect data isn\u2019t necessary to drive strategic insight and action. While that might have been the case with time-series analysis, forecasting using simulation allows companies to do more with less. With simulation software, you aren\u2019t constrained by the hard data points you have for every input; it allows you to enter both qualitative and quantitative information, so you can use human intelligence to make estimates that are later validated for accuracy with observable outcomes. Companies can then use these simulations to test how the market will respond to strategic initiatives by quickly running scenarios before launch. Also, most businesses already have enough collective intelligence within their organization to create a reliable, predictive simulation.<\/p>\n

By unifying analytics, building forecasts and accelerating analytic processes, simulation helps companies build a holistic picture of their business to optimize strategy and maximize revenue. Here are the four types of information that companies need to fuel simulation forecasting and monetize their data investments:<\/p>\n

1. Sales Data: Define success<\/strong><\/h3>\n

The first set of information needed for simulation forecasting is sales data. In building a simulation model, sales data is used to define the market by establishing the outcome you\u2019re trying to influence. That said, simulations can forecast more than sales outcomes in terms of revenue \u2013 they can also simulate a variety of other outcomes tied to sales such as new subscribers, website visits, online application submissions or program enrollments. Whatever the outcome is that you\u2019re measuring, it\u2019s helpful to have the information broken out by segment. If you don\u2019t have this level of detail to start, you can continue to integrate new data into the model to make it more comprehensive over time.<\/p>\n

2. Competitive Data: Paint a full picture of your market<\/strong><\/h3>\n

With simulation forecasting, you are recreating an entire market so you can test how your solution will play out amongst competitors. In order to understand how people within a certain category respond to all of the choices available to them, you will need sales and marketing information for your competition. Competitor data is usually accessible from syndicated sources. If you don\u2019t have access to competitor data, you can use approximate information available from public sources, annual reports or analyses from business experts to build out the competitive market in your simulation.<\/p>\n

3. Customer Data: Understand how your consumer thinks<\/strong><\/h3>\n

The third area of information needed for simulation is customer intelligence. In order to predict the likelihood a consumer will choose one option instead of another, you need to understand how they think. This requires information around awareness, perceptions and the relative importance of different attributes in driving a decision. These datasets are often collected and available through surveys. But even if there isn\u2019t data from a quantitative study, your brand experts can use their judgment to make initial estimates of these values, and the values will later be verified through calibration and forecasting of observed metrics like sales.<\/p>\n

4. Marketing Data: Evaluate the impact of in-market strategies<\/strong><\/h3>\n

Finally, to drive simulation forecasting, companies need data on past marketing activity. This information is essential to understand how messaging in the market has influenced consumer decision making. This can be as simple as marketing investments and impressions broken out by paid, owned and earned activity, or it can be as granular as the tactics and specific media channels within each area.<\/p>\n

Once a company identifies sources for these four types of data, it\u2019s time to find an effective way to monetize it. The best way to get value from your big data is to identify unanswered business questions. With simulation forecasting, reliable answers are accessible \u2013 and you may need less data than you think to get meaningful, trustworthy insight.<\/p>\n","protected":false},"excerpt":{"rendered":"

In the era of big data, the focus has long been on data collection and organization. But despite having access to more data than ever before, companies today are\u00a0reporting\u00a0a low return on their investment in analytics. Something\u2019s not working. Today, business leaders are caught up in concerns that they don\u2019t have enough data, it\u2019s not<\/p>\n","protected":false},"author":476,"featured_media":3532,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[187],"tags":[95],"ppma_author":[2998],"class_list":["post-1452","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-bigdata-cloud","tag-big-data-amp-technology"],"authors":[{"term_id":2998,"user_id":476,"is_guest":0,"slug":"john-pasinski","display_name":"John Pasinski","avatar_url":"https:\/\/secure.gravatar.com\/avatar\/?s=96&d=mm&r=g","user_url":"","last_name":"Pasinski","first_name":"John","job_title":"","description":"John Pasinski is VP of Analytics at Concentric<\/a> leading analytics initiatives for the company and its customers"}],"_links":{"self":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/1452","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/users\/476"}],"replies":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/comments?post=1452"}],"version-history":[{"count":3,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/1452\/revisions"}],"predecessor-version":[{"id":29192,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/1452\/revisions\/29192"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media\/3532"}],"wp:attachment":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media?parent=1452"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/categories?post=1452"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/tags?post=1452"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/ppma_author?post=1452"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}