Part 2
Previously we mentioned some of the Machine Learning models that result beneficial when implementing them in your marketing strategy: Sentiment Analysis, Topic Analysis, recommendations and predictions. The use of different behaviour patters allows a better and more complete understanding of the data encountered. But how does this translate into benefits and actual change for your marketing strategy? You already have the data and it's readable for your team and now it time to see how this will benefit and translate into action.
How to implement into SEO strategy
Build around your most searched terms. By extracting the most searched information within your website you will be able to determine what your clients are looking for. Keyword extraction will provide the ideal workspace to generate and link those keywords to your page SEO. Create new content answering the needs expressed by users.
Using Entity Recognition you can focus on targeting specific needs based on
Location
Age
Organization
Gender
3. Take advantage of user engagement. Review your most viewed posts, information, resources and pages. Target and identify where your clients spend the most time. Target new potential clients by redirecting them through these high-traffic sections and convert them into leads. Take advantage of the possibility of processing and analyzing your information.
Better traffic means more impact. Getting new clients and keep the already existing ones is the main goal for marketers. Take advantage of all the data your clients are already providing when taking important decisions and business operational changes.
According to a survey run by Hubspot over over 60% of marketers measure the success of their content marketing strategy through sales (Source: https://www.hubspot.com/marketing-statistics). So how can you boost those sales with correct data handling?
Two of the main Analytics used in sales are Predictive and Prescriptive analytics.
Predictive is focused on WHAT is going to happen. Predicts behaviour base don previous patterns,trends and activity from the data submitted. Through M.L. user cycles and repetitive behaviours are mapped out.
Having accurate information allows marketers to channel and identify statistics of SALES GROWTH. This allows space for decision making based on data-driven information and avoid unfounded assumptions.
A.I. and M.L become vital to correct sale analysis and decision making. Avoid costly mistakes and expensive changes made without taking clients and users into account. Making analytic tools your best friend in sales allows your team to spot trends, predict new interests, prioritize work on needed areas of improvement.
