Mahr
What Challenged Them?
MAHR – a key player in the engineering sector, faced a significant dilemma centred around the effective organisation of data to enhance their sales processes. The sheer volume of data accumulated from diverse sources over a decade had created a substantial hurdle, impeding the ability to make well-informed decisions.
Here are some Key Issues MAHR faced:
- Quotation Quandary – The sales team generated quotes amounting to millions of pounds.
- Follow-Up Fallout – Inconsistent or minimal follow-up ensued due to the complexity of managing data across multiple systems.
- Financially Unfulfilled – The lack of a unified system resulted in lower conversion rates (and not gaining market share).
This predicament demanded a strategic and efficient approach to marketing and sales data organisation, with the ultimate goal of bolstering the client’s sales performance.
MAHR Data Visibility Requirements:
MAHR sought a comprehensive view of their data, specifically focusing on:
Here are some Key Issues MAHR faced:
- Conversion Projections – Identifying quotes with the highest likelihood of transforming into actual sales. This assessment drew from both historical activity and current customer engagement levels.
- Sales Team Interaction Analysis – Gaining insights into the frequency and recency of interactions between the sales team and customers. This aimed to provide a nuanced understanding of the team’s engagement efforts.
- Purchase History Examination – Examining data on the frequency of machinery purchases by customers. This aspect aimed to shed light on the customer’s buying patterns and preferences over time.
Meeting these requirements was crucial for enabling the client to make informed strategic decisions and enhance their sales effectiveness.
MAHR’s Data Arrangement Criteria:
The client articulated stringent criteria for the organisation of their data, emphasising the need for a more streamlined arrangement to benefit their business. This presented an opportunity to showcase the transformative effects of interconnecting various data sources, including Penta, Salesforce, and aWeber. The sales team required a cohesive and actionable dataset derived from this amalgamation. The specified outputs included:
- Unified Data Set – All records are meticulously matched, consolidating data into a single coherent set.
- Unmatched Records – Identification of records not shared between the two primary systems, Penta and Salesforce.
- Outstanding Quotes – A focus on quotes that were pending and required attention.
- Comprehensive Matched Records – Inclusion of fully matched records alongside recent activity and historical sales data.
- Incompletely Matched Records – Records lacking full matches but supplemented with recent activity and past sales data.
- Orphaned Salesforce Data – Isolation and analysis of data within Salesforce that lacked corresponding matches.
Our Follow-up Actions Comprised:
- Data Refinement – Sales team engagement to update and simplify orphaned and unmatched data, ensuring completeness and accuracy.
- Opportunity Prioritisation – Creation of a curated list featuring the top 50 opportunities, each valued over £2500.
- Identification of Hot Leads – Compilation of a list spotlighting new leads holding outstanding quotes, who had not made prior purchases.
These actions aimed to enhance data quality, prioritise lucrative opportunities and uncover potential avenues for the expansion of business.
The Solution
Our team initiated the task of extracting and synthesising data from diverse origins, including Penta, Salesforce, Past Purchase (spanning approximately 12 years of historical data), and aWeber, utilising the capabilities of the Alteryx platform.
Each data source underwent a meticulous series of actions before culminating in the final merge and subsequent outputs.
Here’s a breakdown of our approach:
1. Field Refinement
- Rectification of word and character formatting across all fields.
- Elimination of non-Western characters.
- Removal of redundant white spaces.
- Exclusion of numerical data in non-numeric fields.
- Standardisation of numerical and text data.
2. Data Cleansing:
- Elimination of duplicate data.
- Aggregation of all points of contact for each company into a unified group.
- Calculation of total values for quotes.
3. Integration Process:
- The initial combination of data from Salesforce and aWeber is based on email addresses.
- Subsequent merging of this amalgamated data with Penta’s dataset.
- Further consolidation with historical sales data.
4. Quality Assurance:
- Visual inspection of the merged data to ensure accuracy.
- Compilation of XLSX files, primed for the client’s perusal, ready for reimporting to prime system.
Logical decisions were applied to the data through comparative analysis, aligning with the desired outcomes. This method allowed us to pinpoint the data configurations most likely to yield the highest conversion rates for MAHR.
The Outcome
The generated datasets crafted a user-friendly and practical roster of prospects, categorising them into highly convertible leads requiring nurturing and those necessitating re-engagement.
By capitalising on this refined data, MAHR gained visibility into segments requiring manual intervention. This strategic approach ensured a simplified process in subsequent runs, minimising, and ideally eliminating orphaned data.
Furthermore, MAHR acquired a valuable resource for future campaigns, enabling targeted outreach to specific industries.
In the ultimate analysis, our efforts facilitated the identification of £4.5 million in potential revenue for the client, displaying the tangible impact of our data optimisation strategies.
Final Thoughts
Data wields immense power when employed effectively in marketing campaigns. However, its mismanagement can ripple through various facets of a company, disrupting not only marketing endeavours but potentially impacting other operational areas.
To avert the pitfalls of suboptimal data management, it is crucial to adhere to best practices. This involves ongoing data cleanup, the application of standards at the point of entry, and regular reviews to ensure data integrity.
The intricate interplay between sales and marketing in the domain of data is inherently complex, demanding strong collaboration between these departments for success.
Clearly outlining the significance and purpose of the data, along with its intended use and anticipated impact on conversions, is essential. This facilitates greater buy-in from the sales team and reduces resistance in future data-driven projects undertaken by us.
These projects may encompass automated nurturing series, predictions and management of prospect conversions, social outreach initiatives, future sales modelling, and more. Establishing a foundation of understanding and collaboration ensures that data becomes a driving force, propelling both sales and marketing towards sustained success.
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We do this for manufacturers every day through our manufacturer marketing.
Why the data problem was a sales problem
Mahr did not come to us with a marketing brief. They came with a decade of data spread across multiple systems, and a sales team generating quotes worth millions with no reliable way to follow them up.
That distinction matters. The instinct with a quote to conversion gap is to generate more quotes. The engineering read is different: if follow-up is inconsistent because the data is fragmented, more quotes make the problem larger, not smaller. Every additional quote enters the same broken process.
So the work started with visibility rather than volume. Getting a decade of accumulated data from diverse sources into one place, arranged so the sales team could see what was live, what had gone quiet, and what was worth chasing. Only then does follow-up become a process rather than a memory test.
This is the pattern behind most of the engineering and manufacturing work we do. The presenting problem is usually conversion. The underlying problem is usually that nobody can see the pipeline clearly enough to act on it.
