---
title: 4 Strategies to Boost Sales with Data Mining
description: Want to increase sales? Your data can help you.
image: https://blog.avenuecode.com/hubfs/pexels-kaboompics-com-6229.jpg
---

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# [4 Strategies to Boost Sales with Data Mining](https://blog.avenuecode.com/4-strategies-to-boost-sales-with-data-mining)

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*perm\_identity* [Tulio Souza](https://blog.avenuecode.com/4-strategies-to-boost-sales-with-data-mining/author/tulio-souza?hsLang=en-us)

*schedule* 10/6/21 2:00 PM

Are you an executive, director, or manager responsible for implementing sales analytics strategies? These four data strategies might just be the key to your business success.

In our last Data Analytics series post, we highlighted [The 6 Pillars of Data Modernization Success](https://blog.avenuecode.com/the-6-elements-of-data-modernization-success?hsLang=en-us). Today, you're going to learn 4 ways to extract business value from an asset you almost certainly already have within your company: data!

Marketing automation software and CRMs have provided incredible value to businesses in every vertical, yet there's often a lot of business value left on the table because the wealth of information these systems contain is underutilized. 

In the hands of skilled data analysts, your sales data can help your business grow to new levels. But the opposite is also true: misinterpreted data can be the bane of your business.

Therein lies the difference between converting a lead or losing it, and for any company trying to expand, every sale counts.

In this article, we'll introduce four advanced sales analytics strategies to implement in your business.

##### 1. Data Warehouse

A data warehouse can act as a unified repository for sales and marketing data, storing information in an organized manner in order to facilitate better understanding of how to create more effective sales strategies.

Data can be extracted from a variety of sources, such as:

1. CRM software;
2. Marketing automation tools;
3. ERP software;
4. Google Analytics;
5. Spreadsheets;
6. Tables; and
7. Any other software used to store client data.

This data may be extracted and arranged in various formats, such as:

1. CSV;
2. XLS;
3. TXT; and
4. JSON.

A properly built data warehouse also carries out processes like quality verification, standardization, and data integration, which in turn bring competitive advantages like confidence in data reliability and agility in decision making.

In terms of data warehousing solutions available on the market, Google Cloud Platform offers [BigQuery](https://cloud.google.com/bigquery?utm_source=google&utm_medium=cpc&utm_campaign=latam-BR-all-pt-dr-SKWS-all-all-trial-e-dr-1009897-LUAC0008678&utm_content=text-ad-none-any-DEV_c-CRE_429238891569-ADGP_Hybrid%20%7C%20SKWS%20-%20EXA%20%7C%20Txt%20~%20Data-Analytics_BigQuery-KWID_43700040482195741-kwd-372661971564&utm_term=KW_bigquery-ST_BigQuery&gclid=Cj0KCQjws4aKBhDPARIsAIWH0JXtv-010l6C69wJftWkVlXudxoL50coN1kde2X7ygwfWNNsMVXZKaQaAoqUEALw_wcB&gclsrc=aw.ds), a serverless, highly scalable, and cost-effective multi-cloud data warehouse designed to promote business agility.

##### 2. Exploratory Data Analysis

Creating your sales analytics data warehouse paves the way for the application of statistical techniques that can answer business questions using data.

Questions that previously seemed impossible to answer are clarified as the analysis progresses.

Using the right tools, reports that used to take a lot of effort to prepare are automated using proprietary data visualization tools.

Exploratory analysis entails formulating and evaluating business hypotheses with data insights, increasing the company's decision-making maturity.

This process can be formulated in programming languages ​​like Python and R using open source frameworks, or in interface software like Power BI, Qlik Sense, and Excel, depending on the resources available to your team.

The important thing is to answer relevant questions to boost business results and to identify opportunities to initiate more complex and profitable projects.

##### 3. Predictive Analysis

It isn't news to anyone that companies are adopting data science applications at an unbelievable speed.

In conjunction with machine learning techniques, these applications enable your company to make very accurate predictions about what will happen in the future, training models based on past events.

This enables companies to answer questions like "How many clients will we acquire next month?" and "Which of my clients will cancel contracts next month (customer churn)?" 

If increasing sales is your goal, the tools you need already exist in your database. 

To help you accomplish this, Google Cloud Platform offers [Vertex AI](https://cloud.google.com/vertex-ai), where you can build, deploy, and scale ML models with pre-trained and custom tooling within a unified AI platform. 

##### 4. Clustering Analysis

How do you get the right message to the right customer at the right time?

Customer segmentation has always been one of the great marketing goals.

Using unsupervised machine learning techniques, you can understand and segment your audience with rich detail and confidence.

Clustering algorithms make it possible to group and understand your customers and leads through various features in your sales management software.

Clustering analysis enables you to group customers who exhibit the same behavior so that you can offer products they're mathematically more likely to buy.

##### How You Can Get Started

Achieving data maturity is a daunting challenge for any organization.

The complexity of a data project often discourages people from undertaking the journey at all, and if it is undertaken, the lack of resources and trained professionals in our market creates huge barriers to success.

Further complications arise because data maturity is a relatively recent goal in the corporate world, so even when mangers can find skilled professionals to hire, they often have little idea how to direct complex projects involving data.

So how do you get started?

Building a data warehouse/data lake is the first step of the journey. 

To do this, your data analysts need to understand which data is available at your organization and ask detailed questions about the data volumetry, velocity, and veracity, as well as the current tools and resources your team uses. This information enables data professionals to design the right solution.

At Avenue Code, we have several data experts who can [help your company](https://www.avenuecode.com/contact/request-consultant) build end-to-end solutions with state-of-the-art tools to boost your sales and your overall business success. 

If you enjoyed today's post, be sure to check out the other blogs in our data analytics series: [The 6 Pillars of Data Modernization Success](https://blog.avenuecode.com/the-6-elements-of-data-modernization-success?hsLang=en-us) and [Modernizing Your Data Warehouse with BigQuery.](https://blog.avenuecode.com/modernizing-your-data-warehouse-with-bigquery?hsLang=en-us)

---

### Author

# Tulio Souza

 Tulio Souza is a Senior Data Scientist at Avenue Code. He is passionate about machine learning, deep learning, and data solutions. In his free time, he likes to play Samba and go surfing.

---

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