What to Look for in a Data Analytics Company

Choosing the right data analytics company can help your business turn its data into a goldmine. Every customer interaction, every ad click, every product page view has valuable information. But synthesizing this data in a way that positively impacts your bottom line while avoiding analysis paralysis can be challenging.

Partnering with an external data analytics company can bring both cost savings and revenue growth. A top notch analytics provider will have deep expertise, technological know-how, and valuable insights from working with a variety of businesses.

In this guide, we'll dive into how you can evaluate a data analytics company to work with. We'll cover essential capabilities, the right questions to ask, and the red flags that signal you should choose someone else. For SaaS or ecommerce businesses of any size, choosing the right analytics partner can be the difference between guessing and knowing exactly how to run and scale your business.

Why Your Business Needs a Specialized Data Analytics Company

Many growing ecommerce and SaaS companies don't have a data problem tracking their data. And as AI advances, this big data is going to become infinite data. The bigger challenge is in efficiently gleaning useful insights from your data. Too often, teams get caught up in tracking, analyzing, and reporting on analytics at the expense of getting actionable insights and taking action on them.

The Real Cost of DIY Analytics

Here's what typically happens when fast-growing companies try to handle their data analytics internally:

Your team gets bogged down in manual work. Someone, such as a marketer, head of sales, sales ops manager, or engineer — who would be better positioned focusing on their actual job — spends 10-15 hours per week pulling data from various sources, copying it into spreadsheets, and then creating visual reports that are outdated the moment they're finished.

Important decisions get delayed. Delays in data synthesis, such as compiling marketing campaign results from different channels and figuring out how they impact the bottom line, can hinder or slow down decision making. You might not have immediately accessible information that shows you how you can reallocate your budget or which ad campaigns to cut.

Tools don't talk to each other. Your data sources operate in silos. Marketing performance in your ad platforms doesn’t connect to sales data in your CRM. Or your ecommerce platform runs separately from your email marketing tool. Each tool provides its own metrics, but you can't get a holistic view of your customer journey or accurately calculate key metrics like true customer acquisition cost.

Critical insights remain hidden. Without integrated analytics, you can't identify patterns that could significantly impact your business. You might have customer segments with dramatically different lifetime values, products with better margins than others, or marketing channels that appear expensive but actually acquire your most valuable customers. These insights exist in your data, but you lack the infrastructure to surface them.

What Changes When You Work with an Analytics Specialist

A specialized data analytics company can help you solve a fundamental problem: how to turn your disconnected data sources into a single source of truth that your entire team can use to make faster, smarter decisions.

The right analytics partner brings three things you can't easily build in-house:

1. Industry-specific knowledge. A dedicated data analytics company, especially one that specializes in your particular industry, whether it’s B2B SaaS or B2C ecommerce, has the advantage of focusing solely on data analytics and gleaning insights from working with other clients. For example, they will have best practices for tracking product-led growth, growing revenue, and predicting churn. Or they’ll know how to properly attribute revenue across various marketing channels and build customer cohort analyses that reveal your true unit economics in ecommerce.
2. Deep technical expertise. Good data analytics companies will also possess advanced technical expertise in their niche. For example, they’ll know how to build data pipelines that automatically pull from Stripe, Salesforce, Segment, Shopify, and ad platforms and then transform the raw data into useful metrics. Ideally, they will have done this dozens of times before.
3. Speed to market. Statistics show that it can take 3-12 months before a new employee starts delivering meaningful value for a company. In addition, there’s the cost of benefits and other expenses that come with hiring and maintaining employees. With a dedicated data analytics company, there’s a startup and maintenance cost, but you can have insights and actionable dashboards in weeks. And even better: they can make tracking ROI from their work transparent through analytics.

Core Capabilities Every Data Analytics Company Should Offer

Each analytics company will have its strengths, weaknesses, and areas of expertise. What you want to look for is a complete solution that addresses your entire analytics profile because one of the biggest data challenges SaaS and ecommerce companies face is disparate data sets. Here are the core capabilities to look for when researching holistic analytics companies:

Analytics Engineering & Data Infrastructure

Before you can analyze your data, you need to collect it, clean it, and make it accessible. This is where analytics engineering comes in.

A qualified data analytics company should be able to build and maintain the infrastructure that powers your analytics. This includes:

ETL (Extract, Transform, Load) pipelines that automatically pull data from all your business systems. Whether you're using Shopify, Salesforce, Stripe, Google Ads, or dozens of other tools, your analytics partner should be able to create automated processes that gather this data on a regular schedule without manual intervention.

Data transformation and cleaning to ensure accuracy and consistency. Raw data from different sources often uses different formats, naming conventions, and definitions. Your analytics partner should be able to normalize this data so that, for example, a "Customer Type 1” means the same thing across all your systems.

Modern data stack expertise including tools like dbt (data build tool) for data transformation, cloud data warehouses like Snowflake or BigQuery for storage, and data integration platforms like Fivetran or Airbyte. You want your analytics company to be using the latest industry standard tools that are reliable, scalable, and cost-effective.

Data quality monitoring to catch issues before they impact your reporting. This includes automated alerts when data looks unusual, validation rules to ensure accuracy, and documentation so your team understands where each metric comes from.

Why does this foundation matter? Because without reliable data infrastructure, everything else falls apart. You can have beautiful dashboards, but if they're pulling incorrect or incomplete data, you'll make decisions based on faulty information.

Business Intelligence & Reporting

Once your data infrastructure is in place, you need tools that can help your team actually use that data. This is where business intelligence (BI) and reporting come in.

Look for a data analytics company that can:

Build custom dashboards tailored to your business. Generic templates rarely work. Your sales team will need different metrics from your marketing team’s. Your executives may want high-level trends, while your operators need granular details. The right partner will create custom analytics dashboards that are designed around how your team actually works.

Create reports that people will actually use. The best dashboards are intuitive, load quickly, and answer specific questions without requiring training. They should be accessible on desktop and mobile, and should update automatically so your team always sees current information.

Work with your preferred BI tools. Whether you're using Looker, Tableau, Power BI, Metabase, or Looker Studio, your analytics partner should have deep expertise in the platforms you prefer. And if you’re starting from scratch, they should also be able to recommend the best tool for your needs.

Provide both real-time and historical analysis. Some decisions require up-to-the-minute data (like monitoring a product launch or ad campaign). Others benefit from historical trends (like seasonal patterns or year-over-year growth). Your analytics solution should support both.

The goal is to provide insights that drive action. Every dashboard should help someone on your team make optimization decisions.

Industry-Specific Analytics Expertise

You want a data analytics company that has experience working with business and data like your. For example:

B2B SaaS companies should look for expertise in:

• Recurring revenue tracking: Accurate ARR and MRR calculations, including expansion revenue, contraction, and churn.
• Product analytics: Activation rates, feature adoption, time-to-value, and engagement metrics.
• Retention and churn analysis: Cohort-based retention, churn prediction models, and identification of at-risk accounts.
• Product-led growth (PLG) metrics: Self-serve conversion rates, free-to-paid conversion, and usage-based triggers.
• Sales efficiency: Lead scoring, sales cycle length, win rates, and pipeline velocity.

For ecommerce companies, look for expertise in:

• Marketing attribution: Understanding which channels drive purchases, not just visitors and clicks, across your entire marketing mix.
• Customer economics: Accurate LTV and CAC calculations, including repeat purchase rates and cohort analysis.
• Inventory analytics: Stock levels, turnover rates, forecasting demand, and identifying slow-moving products.
• Subscription metrics: (for DTC subscription brands) Churn rates, reactivation, subscription value, and retention curves.
• Channel performance: Revenue and profitability by product, category, marketing channel, and customer segment.

A data analytics company with industry-specific expertise will know which metrics matter most for your business model. They'll be capable of understanding the nuances for your types of business.

AI & Advanced Analytics

While not every business needs advanced AI capabilities immediately, it's worth finding a partner who can grow with you. As your data infrastructure matures, you'll want to move beyond descriptive analytics (what happened) to both predictive and prescriptive analytics (what will happen and what should we do about it).

Look for capabilities in:

Predictive modeling such as churn prediction that identifies which customers are likely to cancel, demand forecasting for inventory planning, or revenue forecasting based on pipeline and historical patterns.

You can read more about predictive analytics in our post 75 Ways SaaS Companies Can Use Predictive Analytics.

Optimization algorithms like dynamic pricing models, budget allocation recommendations, or A/B test analysis that determines statistical significance.

Machine learning implementations such as lead scoring models, product recommendation engines, or customer segmentation based on behavior patterns.

An ROI-focused approach that isn’t about implementing AI for the sake of using AI, but rather the ability to identify specific business problems where predictive analytics can drive measurable value, and then building solutions that integrate into your existing workflows.

Not every business needs AI-powered analytics on day one. But as you scale, having a partner who can implement these capabilities becomes increasingly valuable.

Key Questions to Ask Potential Data Analytics Partners

Once you understand what capabilities you need, it's time to evaluate specific providers. Here are some essential questions to ask analytics companies during your vetting process:

Technical Expertise

What tools and platforms do you specialize in?

The right answer to this question depends on your current tech stack. For example, ecommerce companies might want a partner who understands Shopify, Klaviyo, and Meta Ads. Or a SaaS company might need one that knows Salesforce, Segment, and Stripe. Look for specific tool expertise, not just general familiarity.

Can you integrate with our existing tech stack?

Ask about their experience with your specific combination of tools. Have they connected your type of CRM tool to a data warehouse before? Can they pull data from your specific ad platforms? Do they have pre-built connectors or will everything be custom?

How do you ensure data quality and monitoring?

Data quality issues are one of the biggest reasons analytics projects fail. Ask about their approach to validation, error handling, and ongoing monitoring. Do they set up automated alerts? How do they document and share issues with data sources or tools?

What does your analytics infrastructure look like?

Understanding their technical approach can help you evaluate their sophistication. Do they use modern tools like dbt and cloud data warehouses? How do they handle data governance and security? What's their approach to version control and documentation?

Industry Experience

Have you worked with companies in our industry or with our business model?

Specific experience matters. A company that's built analytics for 10 mid-size B2B SaaS companies will understand your challenges better than one that hasn’t. Also, ensure they’re not working with a competitor and will sign an NDA.

Can you show examples of dashboards and reports you've built?

Request to see actual work examples (even with client data anonymized). This can give you a sense of their design philosophy, technical capabilities, and whether their output matches your expectations. Pay attention to whether the dashboards look usable or overwhelming.

What metrics do you typically track for businesses like ours?

This question reveals whether they understand your business model. A good partner should immediately know which KPIs matter for your industry and stage of growth. You should share your specific growth goals so they can tailor their recommendations.

Can you provide client references and case studies?

Do your due diligence and talk to people who have worked with this data analytics provider before. Ask about communication style, ability to meet deadlines, quality of insights, results, and overall satisfaction. Find out what challenges came up and how they were resolved.

Communication & Collaboration

How do you communicate progress and insights?

Analytics projects can feel like black boxes. Ask how often you'll receive updates, what format those updates take, and how they'll explain technical concepts to non-technical team members. You want a partner who communicates proactively, not one you have to chase down.

What is your onboarding process?

Understanding how you’ll get started and how your account will be managed before you decide to work with the company is essential. Ask questions such as: How long will it take to get up and running? What information do you need about my business? How often will we meet and for how long? How do you prioritize which dashboards or reports to build first? A structured onboarding process indicates experience and professionalism.

How quickly can you deliver initial insights?

Time to value matters. Some partners can deliver initial dashboards in 2-3 weeks. Others take months. Ask about their typical timeline and what factors might extend it. Be wary of promises that sound too good to be true.

Who will be our main point of contact?

You want to know who you'll actually be working with, not just who sells you the service. Ask about the team members who'll be assigned to your account, their experience levels, and their availability.

Flexibility & Scalability

Can you scale with our business as we grow?

Your analytics needs will evolve. You might start with basic dashboards but eventually need predictive models. You might add new tools to your tech stack. Ask how the company handles expanding scope and whether they have the resources available to support your growth.

How do you handle changing requirements?

Requirements always change. You'll discover new questions you want answered or realize certain metrics aren't as important as you thought. Ask about their approach to iteration and whether changes require formal change orders or if there's built-in flexibility.

What does ongoing support look like?

Analytics isn't a one-time project. Dashboards need updates, new data sources get added, and tools occasionally break. Understand what's included in ongoing support versus what costs extra. How quickly do they respond to issues? What happens when something goes awry?

How do you stay current with new tools and best practices?

The data analytics landscape is evolving at lightning speed. New tools are emerging, best practices will change, and platform APIs will be updated. Ask how they stay current and how they’ll ensure your analytics infrastructure won’t become outdated.

In-House Team vs. Analytics Agency: What Makes Sense for You?

One of the biggest decisions you'll face is whether to build an internal analytics team or partner with an external agency. Understanding the tradeoffs between the two options can help you make an informed choice.

The Case for an In-House Team

Building an internal analytics team makes sense when:

You need someone deeply embedded in your company culture. Internal team members attend all your meetings, understand your strategy intimately, and build relationships across departments. They’re also not working with other companies. In this case, you might want an internal, full-time person..

You have the budget and timeline to invest in hiring. Building a strong analytics team is expensive and time-consuming, but if you have the resources and can wait 6-12 months to see results, it might be the right path for your organization.

The Case for a Data Analytics Agency

Partnering with an agency makes sense when:

You need expertise immediately. An external data analytics company can start delivering value in weeks, not months. You skip the entire hiring, onboarding, and training process.

You want access to diverse expertise. A good agency brings experience from dozens of similar companies. They've solved your problems before and know what works. You get senior-level expertise across data engineering, analytics, and business intelligence without hiring multiple people.

You need flexibility. Your analytics needs will fluctuate. During a major initiative, you might need more support. During slower periods, you might need less. Agencies can scale their involvement up or down more easily than hiring and firing employees.

You want to avoid management overhead. Managing an internal analytics team requires time and expertise. You need to set priorities, review work, handle performance issues, and keep team members growing in their careers. An external data analytics agency will manage themselves.

Budget predictability matters. With an agency, you’ll know your monthly or project costs upfront and you’ll have a specific contract term. With employees, you have salary, benefits, recruiting costs, training, and tools plus a commitment to long-term work (and therefore costs).

The Hidden Costs of Building In-House

Many companies underestimate what it really takes to build an internal analytics capability. Here are expenses and opportunity costs to consider:

Recruiting takes 3-6 months for each role, often longer for senior positions. During this time, your analytics needs are not getting addressed.

Training and onboarding can take another 2-12 months. It can take new hires up to one year before they become fully productive.

You need multiple roles. One person can't do everything. You typically need a data engineer (to build pipelines), a data analyst (to create reports), and often a BI developer (to build dashboards). That's three positions before you have a complete solution.

Turnover is expensive. Analytics professionals are in high demand. If someone leaves, you’ll have to start the entire cycle over again while also dealing with knowledge loss.

Management overhead is real. Someone will need to manage your internal team, set priorities, and ensure their work aligns with business needs. This usually falls on a busy executive who has other responsibilities. An external data analytics company will handle their own HR, so all you’ll have to do is ensure they’re meeting their deliverables.

Red Flags to Avoid

Not every data analytics company will be a good fit for your business. Here are some warning signs that should make you think twice:

One-size-fits-all approaches. If the provider can only show one type of dashboard vs. custom examples, they may not be the best fit. Your business is unique, and your analytics should reflect that. Look for partners who ask a lot of questions about your specific needs and who can describe customized options.

Lack of industry-specific experience. A company that's never worked with B2B SaaS companies won't understand why expansion revenue matters or how to properly calculate net revenue retention or how to integrate various B2B tech stack components. An agency without ecommerce experience won't know how to properly attribute multi-touch customer journeys or integrating inventory availability with promotional data. Industry experience can significantly reduce risk.

Poor communication or unclear timelines. If getting clear answers during the sales process is difficult, it won't get better once you're a client. Pay attention to how responsive they are, how clearly they explain concepts, and whether they set realistic expectations about timing and costs.

No clear methodology or process. Ask about their approach to projects. If they can't articulate a clear process for how they work, that suggests inexperience. Good partners have repeatable frameworks they've refined over time.

Over-promising on timelines. Building quality analytics infrastructure takes time. If someone promises full dashboards across your entire business in a week, they're either cutting corners or don't understand the complexity. Be skeptical of promises that sound too good to be true.

No proven track record or case studies. New companies need to start somewhere, but your analytics are too important to be someone's learning experience. Look for providers with documented success stories, client testimonials, and examples of work they've done for companies like yours.

Resistance to explaining their work. Analytics can be technical, but good providers can explain what they're doing and why in terms you understand. If someone hides behind jargon or seems annoyed when you ask questions, that's a red flag.

Ignoring data security and governance. Your data contains sensitive business information and might contain customer PII. Any provider you work with should have clear policies around data security, access controls, and compliance. If they're casual about security, look elsewhere.

How to Measure Success with Your Data Analytics Partner

Once you've chosen a partner and started working together, you need to evaluate whether the relationship is delivering value. Here are the key metrics you should track:

Time to First Insights

How quickly did your analytics partner deliver actionable information? While building comprehensive analytics infrastructure takes time, you should see value within the first few weeks. This might be a simple dashboard that answers an important question or insights from analyzing your existing data.

Good partners create quick wins early while building the foundation for long-term success. If you're months into a project without anything useful to show for it, something's wrong.

Adoption Rate Across Your Team

The best dashboards are the ones people actually use. Are your team members checking the dashboards regularly? Are they making decisions based on the insights? Or are the dashboards sitting unused and not acted on?

Track who's using your analytics, how often, and what actions they're taking as a result. Low adoption usually means the dashboards don't answer the right questions or aren't easy enough to use.

Quality of Insights

Are the insights actionable? Do they help you make better decisions? Analytics should do more than just show you what happened. They should help you understand why it happened and what you should do about it.

Pay attention to whether the insights from your analytics partner lead to concrete actions. Have you reallocated your budget based on channel performance? Changed your pricing based on customer segment analysis? Improved retention based on churn insights? These outcomes indicate quality analytics work.

Impact on Key Business Metrics

Ultimately, analytics should impact your bottom line. This might be:

• Improved marketing efficiency (lower CAC, higher ROAS)
• Better retention (reduced churn, increased customer lifetime value)
• Faster growth (shorter sales cycles, higher conversion rates)
• Increased profitability (better pricing, reduced waste)

You should be able to identify specific decisions that were informed by your analytics and led to measurable improvements.

Responsiveness and Communication Quality

How quickly does your partner respond to questions or issues? Do they proactively communicate about updates, problems, or opportunities? Are they easy to work with?

The best analytics partnerships feel collaborative, not transactional. Your partner should feel like an extension of your team, not an external vendor you have to manage.

Reduction in Time Spent on Manual Reporting

One of the most immediate benefits of good analytics infrastructure is time savings. If your team is still spending hours each week pulling data and creating reports manually, your analytics solution isn't fully solving your problems.

Track how much time your team spent on reporting before and after implementing your analytics solution. This time savings has real monetary value and frees your team to focus on strategy instead of data wrangling.

Choosing a Data Analytics Company that Will Augment Your Team

The best data analytics partner should feel like a VIP upgrade for your team. They should bring expertise, cost savings, and results that you can’t get internally while helping you realize your business goals. The companies growing fastest in your industry aren't guessing. They're making the kinds of optimal data-driven decisions that a quality provider can deliver.

Ready to turn your data into actionable insights? Contact Kelihi today for your free consultation.

Frequently Asked Questions

How much does it cost to work with a data analytics company?

Costs vary with scope and complexity. At Kelihi, ongoing engagements start at $7,000 per month. Once you factor in salaries, benefits, recruiting costs, and management overhead, that is usually well below the cost of an in-house hire, and sometimes less than half.

How long does it take to see results from a data analytics company?

You should see a first deliverable, such as a working dashboard or an actionable insight, within 2-3 weeks of starting a project. Building comprehensive analytics infrastructure with multiple data sources, custom dashboards, and advanced features typically takes 2-3 months, but good partners deliver quick wins early while building your long-term foundation.

What's the difference between a data analytics company and a business intelligence consultant?

A data analytics company typically provides end-to-end services including data engineering, infrastructure setup, dashboard development, and ongoing analytics support. Business intelligence consultants often focus primarily on visualization and reporting, assuming you already have clean, accessible data in place.

Do I need a data analytics company if I already have tools like Google Analytics and Shopify reports?

Built-in platform reports are helpful but limited because they only show data from that single platform. A data analytics company connects all your tools together (ads, CRM, ecommerce, email, etc.) to give you a complete view of your customer journey, accurate metrics like true CAC and LTV, and insights that aren't possible when data lives in silos.