Understanding
Forecasting Strategies in AI Realm
Through the introduction of machine learning, data
analytics and advanced artificial intelligence, the CRM space is being
completely altered accordingly. As now there is an opportunity to create
full-scale systems of intelligence, traditionally the world of CRM has been
focused on data storage and minimal engagement. Business firms and
organizations can take advantage of their data and gain valuable insights
leading too clear, actionable forecasts.
The two extremely robust methods that businesses can
utilize: top down and bottom-up. Following approaches can be used personally or
combined. The answer and questions for both about the future needs distinct
types of data and help answer different types of questions.
Across multiple functionalities, fraud detection and
high-class customer relationship management, companies or industries
implementing AI applications will be diversifying as they will be powered with
the ability to analyse information accordingly. This will result in helping a
competitive advantage. In a more human-like fashion, Artificial Intelligence helps in sorting out
problems to complex business problems. In a computer friendly manner, this
reflects adopting features and specifications from human intelligence and
applying them as algorithms.
Following are the given two forecasting methodologies and
how to use them:
Top-Down vs. Bottom-Up
A top-down
approach takes historical information to predict further into the future. We
can say in other words that it leverages aggregate-level information to make
top-level market forecasts. Life is uncertain and it is not helpful in
predicting what will happen tomorrow, but it ca be helpful for anticipating
customer demand, using resources or being proactive about potential future
problems.
On specific
instances, the bottom-up method is more targeted. As every individual event is
predicted and then combined, it is sometimes referred to as a “rollup”
forecast. Along the way, companies track their opportunities and collect tons
of information. For every dataset, this data can be leveraged to estimate
predictions of winning deals and converting leads through a CRM. This might sort
out in sales reps at the ground level to identify key factors and highlight
actions that may help convert a deal. For larger groups and or even the whole
company, more power comes when these insights are rolled-up to generate
forecasts.
Through the
lens of sports, consider the differences in the two approaches. While a
bottom-up forecast can predict every individual game, a top-down approach can
predict how likely a sports team is to make the playoffs.
Implementing the Top-Down
Approach
There are famous machine learning models such like neural
networks do not work for top-down forecasting. Although there are various
traditional statistical methods can outperform high-tech algorithms. The reason
behind is most machine learning do not take into account autocorrelation, a
phenomenon that depicts observations made at close points in time are
more closely related to those made at more different points in time. Let’s take
an example: data from sales for the last year will better guide sales of future
rather than the data from two years ago. However, when building a forecasting
model you must take into account autocorrelation.
Unluckily, the extreme numbers of statistical tools
operate under the assumption that observations in time are independent.
However, the outcomes say a standard model on data with a time-based
correlation can generate misleading results. Although, there are tools that can
handle this type of information, let’s take an example include exponential
smoothing models, moving average models and smoothing splines.
Implementing the Bottom-Up
Approach
For each record in the dataset, it starts with logistic
regression models on historical data that estimates the success probability
when applying a bottom-up forecast. In the information, for estimation the
expected value for every record, combine the predicted propensity of success
with size of every deal, lead and opportunity. More to this, the entire records
can be aggregated in a combination of meaningful ways (i.e. through team,
region etc).
What are its Benefits?
Not only you can initiate forecasts, but you can also
mark fey factors that will help improve the forecasted results. You may invent
that a seven per cent discount in a specific region for a particular product
increases the probability of a close much more than a six per cent discount by
the forecasting work. Such simple insights like this can have a massive impact
on the win rates for sales reps. Business organizations and firms are able to
invest time into increasing the likelihood lower-probability deals to convert
through learning these factors while still making clear forecasts around each
deal’s expected value.
As we move into the next decade, the roll out of external
part of recognition technology is not probably to escalate. The investments by
Government and Corporations are investing in large numbers stating who we are
and analyzing our daily activities and human behaviour.
For powering their forecasting models, companies and
firms need to turn to well-developed statistical tools. It even does help
businesses become data-centric and therefore make smarter choices about the
future while forecasting doesn’t sort out all problems.
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