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5 Questions accountants can ask to deepen client relationships during tax season

December 18, 2014
Read Time: 0 min

At the height of tax season one problem accountants often face is maximizing their client meetings. When an entire firm – and entire industry, for that matter – is racing against the clock and aims to pack as much into each day as possible, it is natural for even the most organized of us to feel a bit disorganized. Aside from the checklists and worksheets that help clients prepare for tax season, now is a great time to double check your own to ensure you are asking the right questions to get the most out of your meetings.

Ask: Do you have a good sense of how your business performed over the past year? How are you tracking your business’s performance monthly? Quarterly?

Opening a conversation with this type of question allows you and your client to discuss how they measure success, particularly because every industry and business can have varying metrics. You’ll learn the business trajectory and can analyze where the business actually stands versus your client’s assumption.

Outlining tactics for productive client meetings now, in December and January, is key to remaining organized before the chaos reigns. Download this worksheet to learn more questions customers of ProfitCents have found helpful to ask during their client meetings.

The five questions included in this worksheet will help you define areas where your clients are potentially interested in learning more about their business and how you can help to improve it. Gain access to questions you should ask that will lead you to deeper relationships with current clients and the opportunity to sell additional value-added services during busy season and beyond.

About the Author


Raleigh, N.C.-based Sageworks, a leading provider of lending, credit risk, and portfolio risk software that enables banks and credit unions to efficiently grow and improve the borrower experience, was founded in 1998. Using its platform, Sageworks analyzed over 11.5 million loans, aggregated the corresponding loan data, and created the largest

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